Providing user assistance in a vehicle based on traffic behavior models

ABSTRACT

Providing user assistance in a vehicle includes evaluating information about the manual operation of the vehicle and information about an environment surrounding the vehicle, and identifying the driving behavior of the vehicle based on the evaluation of the information about the manual operation of the vehicle and the information about the environment surrounding the vehicle. The user assistance further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles, and issuing both prospective instructions and remedial instructions to a user on how to make the driving behavior of the vehicle match the predominating driving behavior of the like population of reference vehicles.

TECHNICAL FIELD

The embodiments disclosed herein generally relate to providing userassistance in vehicles, including vehicles with autonomous operationsystems.

BACKGROUND

Some vehicles include an autonomous operation system under which thevehicle is subject to autonomous operation. In these so-calledautonomous vehicles, a human driver may cede control over one or moreprimary control functions in favor of autonomous operation. Inautonomous operation, the autonomous operation system generates adriving plan for maneuvering the vehicle on a roadway based on detectedinformation about the environment surrounding the vehicle. To executethe driving plan, the autonomous operation system operates vehiclesystems associated with the primary control functions over which thehuman driver has ceded control.

In autonomous vehicles, the capabilities of their autonomous operationsystems may be leveraged to implement various safety technologies, suchas pre-collision systems, blind spot monitors, lane keeping assistantsand the like, to provide user assistance other than autonomousoperation. This user assistance may, moreover, be provided innon-autonomous vehicles using the same or otherwise similar componentstypical of autonomous operation systems.

SUMMARY

Disclosed herein are embodiments of vehicles configured to provide userassistance, and methods of providing user assistance in vehicles, thatinvolve using traffic behavior models as points of reference.

In one aspect, providing user assistance in a vehicle includes facets ofperception and planning/decision making. A planning/decision makingmodule may be used to receive a traffic behavior model that describes apredominating driving behavior of a like population of referencevehicles. While the vehicle is in the midst of manual operation, theplanning/decision making module may also be used to issue, at at leastone interface, prospective instructions to a user on how to make adriving behavior of the vehicle match the predominating driving behaviorof the like population of reference vehicles. A perception module may beused to evaluate information about the manual operation of the vehicleand information about an environment surrounding the vehicle. Aperception module may be used to identify the driving behavior of thevehicle based on the evaluation of the information about the manualoperation of the vehicle and the information about the environmentsurrounding the vehicle. In response to identifying that the drivingbehavior of the vehicle does not match the predominating drivingbehavior of the like population of reference vehicles, theplanning/decision making module may also be used once more to issue, atthe least one interface, remedial instructions to the user on how tomake the driving behavior of the vehicle match the predominating drivingbehavior of the like population of reference vehicles.

In another aspect, providing user assistance in a vehicle includesfacets of perception and planning/decision making. A perception modulemay be used to evaluate information about manual operation of thevehicle and information about an environment surrounding the vehicle.The perception module may also be used to identify, based on theevaluation of the information about the manual operation of the vehicleand the information about the environment surrounding the vehicle, adriving behavior of the vehicle. A planning/decision making module maybe used to receive a traffic behavior model that describes apredominating driving behavior of a like population of referencevehicles. In response to identifying that the driving behavior of thevehicle does not match the predominating driving behavior of the likepopulation of reference vehicles, the planning/decision making modulemay also be used to issue, at at least one interface, an alert to a userprompting the user to implement corrective manual operation under whichthe driving behavior of the vehicle matches the predominating drivingbehavior of the like population of reference vehicles.

In yet another aspect, providing user assistance in a vehicle includesfacets of perception and planning/decision making. While the vehicle isin the midst of manual operation, a perception module may be used toevaluate information about an environment surrounding the vehicle. Theperception module may also be used to identify, based on the evaluationof the information about the environment surrounding the vehicle, atraffic behavior of an object in the environment surrounding thevehicle. A planning/decision making module may be used to receive atraffic behavior model that describes a predominating traffic behaviorof a like population of reference objects. In response to identifyingthat the traffic behavior of the object does not match the predominatingtraffic behavior of the like population of reference objects, theplanning/decision making module may also be used to issue, at at leastone interface, an alert to a user prompting the user to implementdefensive manual operation under which the traffic behavior of theobject is addressed.

These and other aspects will be described in additional detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The various features, advantages and other uses of the presentembodiments will become more apparent by referring to the followingdetailed description and drawing in which:

FIG. 1 includes top views of a vehicle, showing, via block diagrams,components of an autonomous operation system;

FIG. 2 is a diagram of a system for modeling the traffic behavior ofreference objects;

FIG. 3 is a flowchart showing the operations of a process by which thesystem for modeling the traffic behavior of reference objects generatestraffic behavior models that describe, among other things, predominatingdriving behavior and other predominating traffic behavior;

FIG. 4 is a flowchart showing the operations of a process by which theautonomous operation system predicts the future maneuvering of an objectin the environment surrounding the vehicle by extrapolating thepredominating traffic behavior described in a traffic behavior model;

FIG. 5 is an example view of a user of the vehicle out of the vehicle'swindshield, showing an example environment surrounding the vehicle, aswell as example conceptual renderings of outputs that issue, among otherthings, alerts to the user according to the processes of FIG. 4;

FIG. 6 is a flowchart showing the operations of a process by which theautonomous operation system implements autonomous operation of thevehicle under which its driving behavior matches the predominatingdriving behavior described in a traffic behavior model;

FIG. 7 is a flowchart showing the operations of a process by which theautonomous operation system actively trains a user to implement manualoperation of the vehicle under which its driving behavior matches thepredominating driving behavior described in a traffic behavior model;

FIG. 8A is an example view of the user of the vehicle out of thevehicle's windshield, showing an example environment surrounding thevehicle, as well as example conceptual renderings of outputs that issue,among other things, prospective instructions to the user according tothe process of FIG. 7;

FIG. 8B is an example view of a display in the vehicle, showing exampleconceptual renderings of outputs that issue, among other things,remedial instructions to the user according to the process of FIG. 7;

FIG. 9 is a flowchart showing the operations of a process by which theautonomous operation system prompts corrective manual or autonomousoperation of the vehicle under which its driving behavior matches thepredominating driving behavior described in a traffic behavior model;

FIG. 10 is a flowchart showing the operations of a process by which theautonomous operation system prompts defensive manual or autonomousoperation of the vehicle when the traffic behavior of an object in theenvironment surrounding the vehicle does not match the predominatingtraffic behavior described in a traffic behavior model; and

FIG. 11 is an example view of a user of the vehicle out of the vehicle'swindshield, showing an example environment surrounding the vehicle, aswell as example conceptual renderings of outputs that issue, among otherthings, various alerts, remedial instructions and offers of autonomousoperation to the user according to the processes of FIGS. 9 and 10.

DETAILED DESCRIPTION

This disclosure teaches a vehicle with components typical of anautonomous operation system. The vehicle is informed bysituationally-dependent traffic behavior models generated based on theidentified traffic behaviors of reference objects. The traffic behaviormodels describe, among other things, predominating traffic behavior andatypical traffic behavior, including predominating driving behavior andatypical driving behavior. Thus informed, the vehicle providesassistance to a user of the vehicle. In one form of user assistance, auser is actively trained, using either prospective or remedialinstructions, or both, to implement manual operation of the vehicleunder which the vehicle's driving behavior matches the predominatingdriving behavior. In another form of user assistance, corrective manualoperation of the vehicle is prompted, under which the vehicle's drivingbehavior matches the predominating driving behavior, when the drivingbehavior of the vehicle is atypical of the predominating drivingbehavior, or otherwise does not match the predominating drivingbehavior. In another form of user assistance, defensive manual operationof the vehicle is prompted when the traffic behavior of an object in theenvironment surrounding the vehicle is atypical of the predominatingtraffic behavior, or otherwise does not match the predominating trafficbehavior.

Vehicle with an Autonomous Operation System

A representative vehicle 10 is shown in FIG. 1. The vehicle 10 has anexterior and a number of inner compartments. The inner compartments mayinclude a passenger compartment 12, an engine compartment and, for theillustrated vehicle 10, a trunk.

The vehicle 10 may include, among other things, an engine, motor,transmission and other powertrain components housed in its enginecompartment or elsewhere in the vehicle 10, as well as other powertraincomponents, such as wheels. The wheels support the remainder of thevehicle 10. One, some or all of the wheels may be powered by otherpowertrain components to drive the vehicle 10. One, some or all of thewheels may be steered wheels subject to having their steering anglesadjusted to adjust the orientation of the vehicle 10.

The vehicle 10 includes an autonomous operation system 20 under whichthe vehicle 10 is, generally speaking, subject to autonomous operation.Under the autonomous operation system 20, the vehicle 10 may besemi-autonomous or highly automated, for instance.

Autonomous Support Systems.

The autonomous operation system 20 includes various autonomous supportsystems that support autonomous operation of the vehicle 10. Althoughthe autonomous support systems could be dedicated to the autonomousoperation system 20, it is contemplated that some or all of theautonomous support systems may also support other functions of thevehicle 10, including its manual operation.

The autonomous support systems may be or include various vehicle systems30. The vehicle systems 30 may include a propulsion system 32, an energysystem 34, a braking system 36, a steering system 38, a signaling system40, a stability control system 42, a navigation system 44 and anaudio/video system 46, for example, as well as any other systemsgenerally available in vehicles.

The propulsion system 32 includes components operable to accelerate thevehicle 10, as well as maintain its speed. The propulsion system 32 mayinclude, for instance, the engine, motor, transmission and otherpowertrain components, as well as certain vehicle controls, such as acruise control system. The energy system 34 includes components thatcontrol or otherwise support the storage and use of energy by thevehicle 10. The energy source employed by the energy system 34 mayinclude, for instance, gasoline, natural gas, diesel oil and the like,as well as batteries, fuel cells and the like.

The braking system 36 includes components operable to decelerate thevehicle 10, such as brakes, for instance. The steering system 38includes components operable to adjust the orientation of the vehicle 10with respect to its longitudinal direction α or lateral direction β, orboth, by, for example, adjusting the steering angle of one, some or allof the wheels. The signaling system 40 includes components operable tocommunicate driving intentions and other notifications to other vehiclesand their users. The signaling system 40 may include, for instance,exterior lights such as headlights, a left-turn indicator light, aright-turn indicator light, a brake indicator light, a backup indicatorlight, taillights and a running light. The stability control system 42includes components operable to maintain, among other aspects of thestability of the vehicle 10, its proper yaw and pitch, by, for example,actuating brakes and adjusting the power to one, some or all of thewheels powered by other powertrain components to drive the vehicle 10.

The navigation system 44 establishes routes and directions for thevehicle 10 using, for instance, digital maps. The navigation system 44may itself include digital maps, or the navigation system 44 may connectto remote sources for digital maps. In the absence of the navigationsystem 44, the autonomous operation system 20 may connect to remotesources for routes and directions for the vehicle 10.

The audio/video system 46 includes components operable to serve asinterfaces between users of the vehicle 10 and the vehicle 10 itself.The audio/video system 46 may include components operable to detectmechanical and verbal inputs received from a user of the vehicle 10 andtransform those inputs into corresponding input signals. The audio/videosystem 46 may also include components operable to transform signals,such as signals representing media, into tactile, visual and soundoutputs that may be sensed by a user of the vehicle 10. The audio/videosystem 46 may include, for instance, one or more microphones 50, one ormore speakers 52, one or more displays 54 and a projector 56.

The microphones 50 are operable detect, among other sounds waves, verbalinputs from users of the vehicle 10, and transform those verbal inputsinto corresponding input signals. The speakers 52 are operable toreceive, among other signals, signals representing media from theremainder of the audio/video system 46 and the vehicle 10, and transformthose signals into sound outputs that may be heard by users of thevehicle 10. The microphones 50 may be located within the passengercompartment 12 of the vehicle 10 at any location suitable for detectingverbal inputs from a user of the vehicle 10. Similarly, the speakers 52may be located within the passenger compartment 12 of the vehicle 10 atany location suitable for its sound outputs to be heard by a user of thevehicle 10.

The displays 54 are operable to receive, among other signals, signalsrepresenting media from the remainder of the audio/video system 46 andthe vehicle 10, and employ any of various display technologies totransform those signals into visual outputs at their surfaces that maybe seen by users of the vehicle 10. The projector 56, similarly to thedisplays 54, is operable to receive, among other signals, signalsrepresenting media from the remainder of the audio/video system 46 andthe vehicle 10, and employ any of various display technologies totransform those signals into visual outputs that may be projected ontosurfaces, such as the surface of the windshield 58, that may be seen byusers of the vehicle 10. The displays 54 may also include touch screensby which the displays 54 are operable to detect the presence andlocation of mechanical inputs from users of the vehicle 10 at theirsurfaces, and transform those mechanical inputs into corresponding inputsignals. The displays 54 may be configured, for example, to receivethese mechanical inputs via their touch screens directly upon the visualoutputs at their surfaces. The displays 54, similarly to the microphones50 and the speakers 52, may be located within the passenger compartment12 of the vehicle 10 any location suitable for their visual outputs tobe seen by users of the vehicle 10, and for receiving mechanical inputsfrom users of the vehicle 10 via their touch screens.

In addition to the vehicle systems 30, the autonomous support systemsmay be or include a sensor system 60 including one or more sensors. Thesensor system 60 and its sensors may be positioned anywhere in or on thevehicle 10, and may include existing sensors of the vehicle 10, such asbackup sensors, lane keeping sensors and front sensors, for instance. Inthese and other configurations, the sensor system 60 and its sensors maydetect information about the vehicle 10, including without limitationinformation about the operation of the vehicle 10, information about itspassenger compartment 12 and information about the environmentsurrounding the vehicle 10. In the case of information about theenvironment surrounding the vehicle 10, the sensor system 60 and itssensors may detect information about the environment in front of andbehind the vehicle 10 in its longitudinal direction α, as well as to thesides of the vehicle 10 in its lateral direction β.

The sensor system 60 and its sensors may be configured to monitor inreal-time, that is, at a level of processing responsiveness at whichsensing is sufficiently immediate for a particular process ordetermination to be made, or that enables a processor to keep up withsome external process.

The sensors of the sensor system 60 may include one or more vehiclesensors 62, one or more microphones 64, one or more radar sensors 66,one or more sonar sensors 68, one or more lidar sensors 70, one or morepositioning sensors 72 and one or more cameras 74, for example, as wellas any other sensors generally available in vehicles.

The vehicle sensors 62 are operable to detect information about theoperation of the vehicle 10. The vehicle sensors 62 may include, forinstance, speedometers, gyroscopes, magnetometers, accelerometers,barometers, thermometers, altimeters, inertial measurement units (IMUs)and controller area network (CAN) sensors. In these and otherconfigurations of the vehicle sensors 62, the detected information aboutthe operation of the vehicle 10 may include, for example, its locationand motion, including its speed, acceleration, orientation, rotation,direction and the like, as well as elevation, temperature and theoperational statuses of the vehicle systems 30 and their components.

The microphones 64 are operable detect sounds waves, and transform thosesound waves into corresponding signals. Some microphones 64 may belocated to detect sound waves within the passenger compartment 12 of thevehicle 10. These microphones 64 may be the same as, or auxiliary to,the microphones 50 of the audio/video system 46, and may be similarlylocated within the passenger compartment 12 of the vehicle 10. Othermicrophones 64 may be located to detect sound waves in the environmentsurrounding the vehicle 10. These microphones 64 may, accordingly, be atleast partially exposed to the environment surrounding the vehicle 10.

The radar sensors 66, the sonar sensors 68 and the lidar sensors 70 areeach mounted on the vehicle 10 and positioned to have a fields of viewin the environment surrounding the vehicle 10, and are each, generallyspeaking, operable to detect objects in the environment surrounding thevehicle 10. More specifically, the radar sensors 66, the sonar sensors68 and the lidar sensors 70 are each operable to scan the environmentsurrounding the vehicle 10, using radio signals in the case of the radarsensors 66, sound waves in the case of the sonar sensors 68 and lasersignals in the case of the lidar sensors 70, and generate signalsrepresenting objects, or the lack thereof, in the environmentsurrounding the vehicle 10. Among other things about the objects, thesignals may represent their presence, location and motion, includingtheir speed, acceleration, orientation, rotation, direction and thelike, either absolutely or relative to the vehicle 10, or both.

The positioning sensors 72 are operable to identify the position of thevehicle 10. The positioning sensors 72 may implement, in whole or inpart, a GPS, a geolocation system or a local positioning system, forinstance, or any combination of these. For implementing a GPS, thepositioning sensors 72 may include GPS transceivers configured todetermine a position of the vehicle 10 with respect to the Earth via itslatitude and longitude and, optionally, its altitude.

The cameras 74 are operable to detect light or other electromagneticenergy from objects, and transform that electromagnetic energy intocorresponding visual data signals representing objects, or the lackthereof. The cameras 74 may be, or include, one or more image sensorsconfigured for capturing light or other electromagnetic energy. Theseimage sensors may be, or include, one or more photodetectors, solidstate photodetectors, photodiodes or photomultipliers, or anycombination of these. In these and other configurations, the cameras 74may be any suitable type, including without limitation high resolution,high dynamic range (HDR), infrared (IR) or thermal imaging, or anycombination of these.

Some cameras 74 may be located to detect electromagnetic energy withinthe passenger compartment 12 of the vehicle 10. These cameras 74 mayaccordingly be located within the passenger compartment 12 of thevehicle 10. Other cameras 74 may be located to detect electromagneticenergy in the environment surrounding the vehicle 10. These cameras 74may be mounted on the vehicle 10 and positioned to have fields of viewindividually, or collectively, common to those of the radar sensors 66,the sonar sensors 68 and the lidar sensors 70 in the environmentsurrounding the vehicle 10, for example.

In addition to the vehicle systems 30 and the sensor system 60, theautonomous support systems may include a vehicle-to-vehicle (V2V)communication system 76 and a telematics system 78.

The V2V communication system 76 is operable to establish wirelesscommunication with like V2V communication systems in other vehicles inthe environment surrounding the vehicle 10. The V2V communication system76 wirelessly transmits information about the vehicle 10, including itsstate and information detected by the sensor system 60 and its sensors,to other vehicles in the environment surrounding the vehicle 10.Similarly, the V2V communication system 76 wirelessly receives the sameor similar information about other vehicles in the environmentsurrounding the vehicle 10 from their like V2V communication systems.The V2V communication system 76 may implement dedicated short rangecommunication (DSRC), for instance, or other kinds of wirelesscommunication.

The telematics system 78 is operable to establish wireless communicationwith remote computing devices, such as servers. The telematics system 78wirelessly transmits information about the vehicle 10, including itsstate and information detected by the sensor system 60 and its sensors,to remote computing devices. The telematics system 78 also wirelesslyreceives any variety of information from remote computing devices. Thetelematics system 78 may implement Internet or cellular communication,for instance, to establish wireless communication with remote computingdevices over the Internet or a cellular network, as the case may be, orother kinds of wireless communication.

ECU or Other Computing Device.

In addition to its autonomous support systems, the autonomous operationsystem 20 includes one or more processors 80, a memory 82 and one ormore modules 84. Together, the processors 80, the memory 82 and themodules 84 constitute a computing device to which the vehicle systems30, the sensor system 60, the V2V communication system 76, thetelematics system 78 and any other autonomous support systems arecommunicatively connected. Although this computing device could bededicated to the autonomous operation system 20, it is contemplated thatsome or all of its processors 80, its memory 82 and its modules 84 couldalso be configured as parts of a central control system for the vehicle10, such as a central electronic control unit (ECU).

The processors 80 may be any components configured to execute any of theprocesses described herein or any form of instructions to carry out suchprocesses or cause such processes to be performed. The processors 80 maybe implemented with one or more general-purpose or special-purposeprocessors. Examples of suitable processors 80 include microprocessors,microcontrollers, digital signal processors or other forms of circuitrythat can execute software. Other examples of suitable processors 80include without limitation central processing units (CPUs), arrayprocessors, vector processors, digital signal processors (DSPs),field-programmable gate arrays (FPGAs), programmable logic arrays(PLAs), application specific integrated circuits (ASICs), programmablelogic circuitry or controllers. The processors 80 can include at leastone hardware circuit (e.g., an integrated circuit) configured to carryout instructions contained in program code. In arrangements where thereare multiple processors 80, the processors 80 can work independentlyfrom each other or in combination with one another.

The memory 82 is a non-transitory computer readable medium. The memory82 may include volatile or non-volatile memory, or both. Examples ofsuitable memory 82 includes RAM (Random Access Memory), flash memory,ROM (Read Only Memory), PROM (Programmable Read-Only Memory), EPROM(Erasable Programmable Read-Only Memory), EEPROM (Electrically ErasableProgrammable Read-Only Memory), registers, magnetic disks, opticaldisks, hard drives or any other suitable storage medium, or anycombination of these. The memory 82 includes stored instructions inprogram code. Such instructions can be executed by the processors 80 orthe modules 84. The memory 82 may be part of the processors 80 or themodules 84, or may be communicatively connected the processors 80 or themodules 84.

The modules 84 are employable to perform various tasks in the vehicle10. Generally speaking, the modules 84 include instructions that may beexecuted by the processors 80. The modules 84 can be implemented ascomputer readable program code that, when executed by the processors 80,execute one or more of the processes described herein. Such computerreadable program code can be stored on the memory 82. The modules 84 maybe part of the processors 80, or may be communicatively connected theprocessors 80.

Autonomous Driving Module.

The modules 84 may include, for example, an autonomous driving module90. The autonomous driving module 90 generates driving plans formaneuvering the vehicle 10 on roadways based on the information aboutthe vehicle 10, including information detected by the sensor system 60and its sensors, and executes the driving plans by operating theappropriate vehicle systems 30. In this so-called autonomous operationof the vehicle 10, its human driver will have ceded control over one ormore primary control functions in favor of autonomous operation. Theseprimary control functions may include propulsion, or throttle, brakingor steering, for instance, or any combination of these. The vehiclesystems 30 operated by the autonomous driving module 90 include thoseassociated with the primary control functions over which the humandriver has ceded control.

Among other sub-modules, the autonomous driving module 90 may include aperception module 92, a planning/decision making module 94 and a controlmodule 96.

The perception module 92 gathers and evaluates information about thevehicle 10, including information detected by the sensor system 60 andits sensors and information about other vehicles communicated from theV2V communication system 76, as well as information sourced from digitalmaps. In the case of information about the environment surrounding thevehicle 10, the perception module 92 may, as part of its evaluation,identify objects in the environment surrounding the vehicle 10,including their properties. These properties may include, among otherthings about the objects, their presence, location and motion, includingtheir speed, acceleration, orientation, rotation, direction and thelike, either absolutely or relative to the vehicle 10, or both. In caseswhere these objects are other vehicles, the perception module 92 mayadditionally, or alternatively, identify these things, as well as thestates of the other vehicles, from the information about the othervehicles communicated from the V2V communication system 76.

The perception module 92 may discriminate between different objects andindividually track different objects over time. Either on initialdetection or after tracking them over time, the perception module 92 mayclassify objects to account not only for roadways, features of roadways,such as lane markings, and obstacles on or around roadways, such asother vehicles, but also for surrounding ground, pedestrians, bicycles,construction equipment, road signs, buildings, trees and foliage, forinstance.

Either alone or in combination with its identification andclassification of objects in the environment surrounding the vehicle 10,the perception module 92 may identify the location of the vehicle 10 inthe environment surrounding the vehicle 10. For example, the perceptionmodule 92 may implement localization techniques that match identifiedobjects in the environment surrounding the vehicle 10, as well as theirproperties, to those reflected in digital maps as part of an overall 3Droad network. The autonomous driving module 90 may itself includedigital maps, for instance, or the perception module 92 may connect tothe navigation system 44 or to remote sources for digital maps.Implementing these or other localization techniques, the perceptionmodule 92 may identify, among other aspects about the location of thevehicle 10 in the environment surrounding the vehicle 10, the locationof the vehicle 10 on roadways.

The planning/decision making module 94, based on the evaluation of theinformation about the vehicle 10 by the perception module 92, generatesdriving plans for maneuvering the vehicle 10 on roadways. The drivingplans may be, more specifically, for performing driving maneuvers. Thedriving plans may be part of, or augment, larger but otherwise analogousand similarly generated driving plans for maneuvering the vehicle 10 onroadways.

The driving plans may account for any objects in the environmentsurrounding the vehicle 10, as well as their properties, for example.Particularly in the case of obstacles on or around roadways, the drivingplans may account for their predicted future maneuvering along theroadways. Accordingly, as part of its generation of driving plans, theplanning/decision making module 94 may predict the future maneuvering ofobstacles along roadways. The predicted future maneuvering of anobstacle along a roadway may be based on its presence, location andmotion, as identified by the perception module 92, as well as how theperception module 92 classifies the obstacle and tracks it over time.

The driving plans themselves, as well as underlying predictions of thefuture maneuvering of obstacles along roadways, may also account fordifferent lane positions and traffic rules, such as speed limits,priorities at intersections and roundabouts, stop line positions and thelike. The autonomous driving module 90 may itself include digital mapsreflecting these lane positions and traffic rules as part of an overall3D road network, for instance, or the planning/decision making module 94may connect to the navigation system 44 or to remote sources for digitalmaps.

The control module 96 operates the appropriate vehicle systems 30 toexecute the driving plans generated by the planning/decision makingmodule 94. The control module 96 may send control signals to the vehiclesystems 30 or may directly send control signals to actuators thatoperate their components, or both.

Modeling the Traffic Behavior of Reference Objects

A system 200 for modeling the traffic behavior of reference objects isshown in FIG. 2. The system 200 supports the operations of a process 300shown in FIG. 3 that involves using information about reference objectsto identify their traffic behavior and generate traffic behavior models.

System Overview.

The reference objects include any combination reference vehicles (e.g.,cars, trucks, motorcycles and the like), reference bicycles, referencepedestrians, reference animals and any other objects that are controlledin traffic. Reference vehicles and reference bicycles could, forinstance, be manually operated or, in other words, manually driven andridden, respectively. Reference vehicles could also, for instance, beautonomously operated or, in other words, autonomously driven. Referencepedestrians and reference animals are, on the other hand,self-ambulated.

To support the generation of robust traffic behavior models, the numberof reference objects is as large as possible. In any event, thereference objects include various like populations of reference objectsas later-identified objects in the environment surrounding the vehicle10, as well as at least one like population of reference vehicles as thevehicle 10.

For purposes of detecting the information about the reference objects,the system 200 includes any combination of the vehicle 10 itself,detection vehicles 210 a-n (collectively, the detection vehicles 210)and a roadway sensor system 235. The reference objects are, accordingly,objects in the respective environments surrounding the vehicle 10, thedetection vehicles 210 and the roadway sensor system 235. The detectionvehicles 210 also serve as reference objects and, more specifically, asreference vehicles. The information the about reference objects used togenerate the traffic behavior models may span any amount of time up toand including a current time. Accordingly, this information may beeither historical or real-time, or both.

For purposes of gathering the information the about reference objectsfor evaluation, the system 200 includes any combination of the vehicle10 and a traffic behavior server 245. The system 200 also includes awireless communication system 255, such as the Internet or a cellularnetwork, by which wireless communication is established with the trafficbehavior server 245.

The detection vehicles 210 are equipped, in whole or in part, similarlyto the vehicle 10. The detection vehicles 210 include various respectivevehicle systems 230 a-n (collectively, the respective vehicle systems230), and respective sensor systems 260 a-n (collectively, therespective sensor systems 260). The respective vehicle systems 230 areoperationally and otherwise similar to the vehicle systems 30 in thevehicle 10. Likewise, the respective sensor systems 260 areoperationally and otherwise similar to the sensor system 60 in thevehicle 10. The respective sensor systems 260 are accordingly operableto detect information about the environments surrounding the detectionvehicles 210, including information about reference objects, such astheir presence, location and motion, as well as information about theenvironments surrounding the reference objects. The respective sensorsystems 260 are also operable to detect, among other information aboutthe detection vehicles 210, information about the operation of thedetection vehicles 210, such as their location and motion.

The respective vehicle systems 230 and the respective sensor systems 260may, like their counterparts in the vehicle 10, be included in thedetection vehicles 210 as autonomous support systems. In any event, one,some or all of the respective vehicle systems 230 and the respectivesensor systems 260 may support, among other functions of the detectionvehicles 210, their manual operation.

The detection vehicles 210 also include respective V2V communicationsystems 276 a-n (collectively, the respective V2V communication systems276), and respective telematics systems 278 a-n (collectively, therespective telematics systems 278). The respective V2V communicationsystems 276 are operationally and otherwise similar to the V2Vcommunication system 76 in the vehicle 10, and the respective telematicssystems 278 are operationally and otherwise similar to the telematicssystem 78 in the vehicle 10.

If the detection vehicles 210 are in the environment surrounding thevehicle 10, the respective V2V communication systems 276, similarly totheir counterparts in the vehicle 10, wirelessly transmit informationabout the detection vehicles 210 to the vehicle 10. Accordingly, intheir role as reference objects, information about the detectionvehicles 210 is both detected by the sensor system 60 and communicatedfrom the respective V2V communication systems 276 as part of a largercollection of information about the vehicle 10 and, more specifically,as part of a larger collection of information about the environmentsurrounding the vehicle 10. The respective telematics systems 278, liketheir counterparts in the vehicle 10, wirelessly transmit informationabout the detection vehicles 210 to remote computing devices, such asthe traffic behavior server 245, via the wireless communication system255.

The roadway sensor system 235 includes one or more sensors positionedanywhere on or along roadways that reference objects are on. The roadwaysensor system 235 and its sensors are operationally and otherwisesimilar to the sensor system 60 and its sensors in the vehicle 10. Theroadway sensor system 235 may accordingly include, for example, anycombination of radar sensors, sonar sensors, lidar sensors, positioningsensors and cameras that are each operable to generate signalsrepresenting information about the environment surrounding the roadwaysensor system 235, including information about reference objects, suchas their presence, location and motion, as well as information about theenvironments surrounding the reference objects.

The roadway sensor system 235 is operable to establish wirelesscommunication with remote computing devices, such as servers. Theroadway sensor system 235 may implement Internet or cellularcommunication, for instance, to establish wireless communication withremote computing devices over the Internet or a cellular network, as thecase may be, or other kinds of wireless communication. Accordingly, theroadway sensor system 235 wirelessly transmits information aboutreference objects to remote computing devices, such as the trafficbehavior server 245, via the wireless communication system 255.

The traffic behavior server 245 includes one or more processors 280, amemory 282 and one or more modules 284, including a perception module292, that together constitute a computing device. The processors 280,the memory 282, the modules 284 generally and the perception module 292specifically are operationally and otherwise similar to the processors80, the memory 82, the modules 84 and the perception module 92,respectively, in the vehicle 10.

In the system 200, the vehicle 10 and the traffic behavior server 245are in mutual communication both with each other, and with the detectionvehicles 210 and the roadway sensor system 235. In the process 300, theinformation about the reference objects may be gathered for evaluationeither by the perception module 92 in the vehicle 10, or by theperception module 292 in the traffic behavior server 245, or both.Moreover, any number of evaluation tasks may be shared between theperception module 92 and the perception module 292. Accordingly, theevaluation tasks in operations 304-310 are described as being performedby the perception modules 92, 292.

Notwithstanding, in some implementations, the process 300 may beperformed onboard the vehicle 10. In one such onboard implementation,the reference objects, including detection vehicles 210 serving asreference objects, are limited to those in the environment surroundingthe vehicle 10. In this onboard implementation, the information aboutthe reference objects is sourced either from the vehicle 10 or from thedetection vehicles 210, or both, and the gathering and evaluation tasksin operations 302-310 are performed in real-time by the perceptionmodule 92 in the vehicle 10.

Identifying the Traffic Behaviors of the Reference Objects.

As a prerequisite to generating traffic behavior models, the informationabout the reference objects is gathered by the perception modules 92,292 in operation 302. In operations 304 and 306, this information isevaluated by the perception modules 92, 292 to identify the trafficbehaviors of the reference objects. These parts of the process 300repeat, so that the traffic behavior models are continuously updatedwith new information about the reference objects.

In their prerequisite evaluation of the information about the referenceobjects, the perception modules 92, 292, in operation 304, identify andcatalog the traffic maneuvers performed by the reference objects. Thesetraffic maneuvers are driving maneuvers, for reference vehicles, bikingmaneuvers, for reference bicycles, and walking maneuvers, for referencepedestrians and reference animals. In operation 306, the perceptionmodules 92, 292 quantify attributes of how the reference objects performthe traffic maneuvers.

Among other information about the reference objects, the trafficmaneuvers performed by the reference objects are identified from theirlocation and motion, as well as from information about the environmentssurrounding the reference objects. In the case of information about theenvironments surrounding the reference objects, the identification ofthe traffic maneuvers performed by the reference objects is informed byinformation sourced from digital maps, including different lanepositions and traffic rules, as well as the location and motion ofobjects in the environments surrounding the reference objects, includingobstacles such as vehicles.

In cases where the information about the reference objects is sourcedfrom either the vehicle 10 or the roadway sensor system 235, or both,the perception modules 92, 292 may identify and track the referenceobjects, classify them, and identify their location and motion. In thesecases, the perception modules 92, 292 may similarly identify, track andclassify objects in the environments surrounding the reference objects,including obstacles such as vehicles, and identify their location andmotion. For a given detection vehicle 210 in its role as a referenceobject, such obstacles may include other detection vehicles 210. Incases where the information about the reference objects is sourced fromthe detection vehicles 210 in their roles as reference objects, thesourced information may already include the location and motion of thedetection vehicles 210 for identification by the perception modules 92,292. In these cases, for a given detection vehicle 210 in its role as areference object, similar information about other detection vehicles 210may be the basis for identifying them as obstacles in the environmentsurrounding the detection vehicle 210, as well as for identifying theirlocation and motion.

Most of the traffic maneuvers performed by the reference objects will bepre-defined standard traffic maneuvers. For reference vehicles and theirdriving maneuvers, standard driving maneuvers may be, or include,traversing intersections, including right-hand turning and left-handturning through intersections, mergers, lane changes and takeovers, forinstance. Particularly in cases of manual operation, some of the trafficmaneuvers performed by the reference objects will be non-standardtraffic maneuvers. For reference vehicles and their driving maneuvers,non-standard driving maneuvers may be, or include, overtakingdouble-parked vehicles, U-turning through intersections and left-handturning through intersections, when first in line, ahead of trafficmoving in the opposite direction, for instance. In many cases,non-standard traffic maneuvers will be specific to certain populationsof reference objects.

To support the generation of situationally-dependent traffic behaviormodels, the traffic maneuvers performed by the reference objects arecataloged. The traffic maneuvers performed by the reference objects maybe cataloged by the type of reference object (e.g., vehicle, bicycle,pedestrian and animal), any sub-types of the reference object (e.g., fora reference vehicle, car, truck, motorcycle, etc.), operation (e.g.,manual or autonomous for a reference vehicle), location, time of day,roadway conditions, traffic conditions and weather conditions, forinstance, or any combination of these.

Either alone or in combination with their identification and catalogingof the traffic maneuvers performed by the reference objects, theperception modules 92, 292 quantify one or more attributes of how thereference objects perform the traffic maneuvers.

The attributes of how the reference objects perform the trafficmaneuvers are objective and quantifiable. These attributes may includethe trajectories (i.e., driving paths, for vehicles, biking paths, forbicycles, and walking paths, for pedestrians and animals) of thereference objects along roadways associated with their performance ofthe traffic maneuvers. These attributes may also include the speed,acceleration and orientation of the reference objects along roadways,for instance, associated with their performance of the trafficmaneuvers.

The attributes of how the reference objects perform the trafficmaneuvers may moreover include relationships between the referenceobjects and the environments surrounding the reference objectsassociated with their performance of the traffic maneuvers. Theenvironments surrounding the reference objects includes different lanepositions and traffic rules, as well as objects in the environmentssurrounding the reference objects. Accordingly, these relationshipsinclude things such as lane offsets, proximity to objects on roadwaysand approach to objects on roadways, for instance.

Generating traffic behavior models.

In operations 308 and 310, the perception modules 92, 292 generate oneor more traffic behavior models. Each traffic behavior model describesboth predominating traffic behavior and atypical traffic behavior. For agiven traffic behavior model, the predominating traffic behavior is thepredominating traffic behavior of a like population of referenceobjects. The atypical traffic behavior, on the other hand, is trafficbehavior atypical of the predominating traffic behavior of the likepopulation of reference objects.

The traffic behavior models are used as points of reference for variousobjects in the environment surrounding the vehicle 10, as well as forthe vehicle 10 itself. The like population of reference objects, andrelated aspects of a given traffic behavior model, depend on the pointof reference for which it is used.

Generally speaking, for an object in the environment surrounding thevehicle 10, a traffic behavior model is one that describes thepredominating traffic behavior and the atypical traffic behavior of alike population of reference objects as the object. More specifically,for another vehicle, a traffic behavior model describes thepredominating driving behavior and the atypical driving behavior of alike population of reference vehicles. For a bicycle, a traffic behaviormodel describes the predominating biking behavior and the atypicalbiking behavior of a like population of reference bicycles. And, for apedestrian or an animal, a traffic behavior model describes thepredominating walking behavior and the atypical walking behavior of alike population of reference pedestrians or reference animals, as thecase may be.

For the vehicle 10 itself, a traffic behavior model describes thepredominating driving behavior and the atypical driving behavior of alike population of reference vehicles. Such a traffic behavior model maybe the same as or different from the one used as a point of referencefor another vehicle in the environment surrounding the vehicle 10, andvice versa.

A given traffic behavior model may be dedicated to describing thepredominating traffic behavior and the atypical traffic behavior of alike population of reference objects as a particular object in theenvironment surrounding the vehicle 10, or may describe these things aspart (e.g., a sub-model) of a larger traffic behavior model thatdescribes additional traffic behaviors. Likewise, a given trafficbehavior model may be dedicated to describing the predominating drivingbehavior and the atypical driving behavior of a like population ofreference vehicles as the vehicle 10, or may describe these things aspart (e.g., a sub-model) of a larger traffic behavior model thatdescribes additional driving behaviors or other traffic behaviors.

Operations 308 and 310 are included in a repeating part of the process300. Accordingly, the traffic behavior models are generated on anobject-by-object basis for the objects in the environment surroundingthe vehicle 10. Also, for both the objects in the environmentsurrounding the vehicle 10 and the vehicle 10 itself, each trafficbehavior model describes both predominating traffic behavior andatypical traffic behavior with reference to a like population ofreference objects. As a result, the traffic behavior models aresituationally-dependent. Specifically, as the vehicle 10 encounters newsituations while being maneuvered on roadways, the traffic behaviormodels are continuously updated, replaced or otherwise regenerated torefer to new like populations of reference objects, as well as theirpredominating traffic behaviors and atypical traffic behaviors. Thetraffic behavior models may, for instance, be regenerated as the vehicle10 encounters any combination of switches between manual and autonomousoperation, new locations, new times of day, new roadway conditions, newtraffic conditions and new weather conditions.

Predominating traffic behavior falls within a larger window of ruleabiding, safe and otherwise feasible traffic behavior. The window offeasible traffic behavior usually allows for traffic behaviors rangingfrom cautious to aggressive. Atypical traffic behavior may or may notfall within this window.

Notwithstanding the range of feasible traffic behavior and, inparticular, driving behavior, according to this disclosure, the drivingbehavior of the vehicle 10 may not only fall within the window offeasible driving behavior, but may also match the predominating drivingbehavior of a like population of reference vehicles. Various benefitsmay be realized as a result. For example, when its driving behaviormatches the predominating driving behavior, the future maneuvering ofthe vehicle 10 along roadways will be rightly predicted, both on behalfof the users of other vehicles in the environment surrounding thevehicle 10, and on behalf of the other vehicles themselves. The users ofthe other vehicles will also perceive the vehicle 10 as beingpredictable and, as a result, will not become uncomfortable with it.Even if the users of the other vehicles are not disposed to becominguncomfortable with the vehicle 10 as a result of its perceivedunpredictability, they will at least not become tiresome of the vehicle10, or exhibit more aggressive driving behavior toward the vehicle 10 asa result. Additionally, when its driving behavior matches thepredominating driving behavior, the vehicle 10 will not missopportunities to perform driving maneuvers.

All of these benefits flow from obviating problems that would otherwisehappen in cases where the driving behavior of the vehicle 10, althoughfalling within the window of feasible driving behavior, does notmoreover match the predominating driving behavior of a like populationof reference vehicles. As indicated above, these problems could includewrongful predictions of the future maneuvering of the vehicle 10,perceptions of unpredictability and resulting uncomfortableness with thevehicle 10, tiresomeness and resulting more aggressive driving behaviortoward the vehicle 10, and missed opportunities for the vehicle 10 toperform driving maneuvers. Notably, many of these problems wouldotherwise happen not only in cases where the driving behavior of thevehicle 10 is too aggressive compared to the predominating drivingbehavior, but also in cases where the driving behavior of the vehicle 10is too cautious.

Various benefits may also be realized because, according to thisdisclosure, the vehicle 10 is aware when the manual driving behavior ofthe vehicle 10 is atypical of the predominating driving behavior of alike population of reference vehicles, or otherwise does not match thepredominating driving behavior of the like population of referencevehicles. The vehicle 10 may pass this awareness to its user in thecontext of the predominating driving behavior. Relatedly, with thisawareness, the vehicle 10 may timely prompt appropriate correctiveoperation of the vehicle 10, under which its driving behavior matchesthe predominating driving behavior, either by its user via manualoperation, or by itself via autonomous operation.

Various benefits may be also realized because, according to thisdisclosure, the vehicle 10 is aware, on an object-by-object basis, ofthe predominating traffic behaviors and the atypical traffic behaviorsof like populations of reference objects as objects in the environmentsurrounding the vehicle 10. The vehicle 10 may pass this awareness toits user, for example, in the form of predictions of the futuremaneuvering of the objects along roadways reached by extrapolating thepredominating traffic behaviors. The vehicle 10 is further aware whenthe traffic behaviors of objects in the environment surrounding thevehicle 10 are atypical of the predominating traffic behaviors, orotherwise do not match the predominating traffic behaviors. The vehicle10 may pass this awareness to its user in the context of thepredominating traffic behaviors. Relatedly, with this awareness, thevehicle 10 may timely prompt appropriate defensive operation of thevehicle 10, under which the traffic behaviors of the objects areaddressed, either by its user via manual operation, or by itself viaautonomous operation.

In operation 308, the perception modules 92, 292 identify an appropriatelike population of reference objects. The identification of the likepopulation of reference objects is based on the cataloging of thetraffic maneuvers performed by the reference objects.

Generally speaking, for an object in the environment surrounding thevehicle 10, the like population of reference objects consists of thoseof the reference objects situated the same as or otherwise similarly tothe object for purposes of performing traffic maneuvers. These trafficmaneuvers are driving maneuvers, for vehicles, biking maneuvers, forbicycles, and walking maneuvers, for pedestrians and animals. For thevehicle 10 itself, the like population of reference objects is a likepopulation of reference vehicles that consists of those of the referencevehicles situated the same as or otherwise similarly to the vehicle 10for purposes of performing driving maneuvers.

For an object in the environment surrounding the vehicle 10, the likepopulation of reference objects could, for instance, be those of thereference objects in the same location as the object. In other words,the like population of reference objects could be a local population ofreference objects. The location may be one geographic area or anycombination of geographic areas, at any varying level of granularity,such as city, municipality, neighborhood, roadway, intersection and thelike. Additionally, or alternatively, the like population of referenceobjects could be any combination of those of the reference objects, forinstance, maneuvering on roadways at the same time of day as the object,maneuvering on roadways under the same roadway conditions as the object,maneuvering on roadways under the same traffic conditions as the objectand maneuvering on roadways under the same weather conditions as theobject. Additionally, or alternatively, the like population of referenceobjects could be those of the reference objects, for instance, havingthe same sub-type as the object (e.g., car, truck, motorcycle, etc. inthe case of other vehicles). Additionally, or alternatively, for othervehicles, the like population of reference vehicles could be those ofthe reference vehicles, for instance, under the same manual orautonomous operation, as the case may be, as the other vehicles. It willbe understood that aspects of the situation of the vehicle 10 (e.g.,location, time of day, roadway conditions, traffic conditions, weatherconditions, etc.) may be used as proxies for corresponding aspects ofthe situations of the objects in the environment surrounding the vehicle10.

For the vehicle 10, the like population of reference vehicles could, forinstance, be those of the reference vehicles in the same location as thevehicle 10. In other words, the like population of reference vehiclescould be a local population of reference vehicles. The location may,once again, be one geographic area or any combination of geographicareas, at any varying level of granularity, such as city, municipality,neighborhood, roadway, intersection and the like. Additionally, oralternatively, the like population of reference vehicles could be anycombination of those of the reference vehicles, for instance,maneuvering on roadways at the same time of day as the vehicle 10,maneuvering on roadways under the same roadway conditions as the vehicle10, maneuvering on roadways under the same traffic conditions as thevehicle 10 and maneuvering on roadways under the same weather conditionsas the vehicle 10. Additionally, or alternatively, the like populationof reference vehicles could be those of the reference vehicles, forinstance, having the same sub-type as the vehicle 10 (e.g., car, truck,motorcycle, etc.). Additionally, or alternatively, while the vehicle 10is in the midst of manual operation, the like population of referencevehicles could be those of the reference vehicles, for instance,likewise under manual operation.

In operation 310, the perception modules 92, 292 generate a trafficbehavior model. The traffic behavior model, as part of its descriptionof the predominating traffic behavior of a like population of referenceobjects, describes the traffic maneuvers performed by the likepopulation of reference objects, as well as the predominating attributesof how the like population of reference objects performs the trafficmaneuvers. The traffic behavior model, as part of its description of thetraffic behavior atypical of the predominating traffic behavior of thelike population of reference objects, describes the atypical attributesof how the like population of reference objects performs the trafficmaneuvers, as well as the traffic maneuvers not performed by the likepopulation of reference objects. Accordingly, in operation 310, theperception modules 92, 292 identify these things and incorporate theirdescriptions into the traffic behavior model.

The traffic maneuvers performed by the like population of referenceobjects may include, for instance, those that are performed by the likepopulation of reference objects at or above a predetermined rate orfrequency. These traffic maneuvers will include most if not all standardtraffic maneuvers, and possibly, depending on the overall trafficbehavior of the like population of reference objects, some non-standardtraffic maneuvers. The traffic maneuvers not performed by the likepopulation of reference objects, on the other hand, will include anyremaining standard traffic maneuvers, as well as any non-standardtraffic maneuvers performed by the like population of reference objectsbut not at or above the predetermined rate or frequency.

The attributes of how the like population of reference objects performsthe traffic maneuvers may be predominating if, for instance, theattributes are the statistically median among those of the likepopulation of reference objects associated with its performance of thetraffic maneuvers. On the other hand, the attributes of how the likepopulation of reference objects performs the traffic maneuvers may beatypical if, for instance, the attributes are statistically outlyingamong those of the like population of reference objects associated withits performance of the traffic maneuvers.

Accordingly, the traffic behavior model may be statistical, anddescribe, for the traffic maneuvers performed by the like population ofreference objects, the statistically median traffic behavior of the likepopulation of reference objects and, more specifically, thestatistically median attributes of how the like population of referenceobjects performs the traffic maneuvers. The statistical traffic behaviormodel also describes the statistically outlying traffic behavior of thelike population of reference objects and, more specifically, thestatistically outlying attributes of how the like population ofreference objects performs the traffic maneuvers.

In the statistical traffic behavior model, the predominating attributesof how the like population of reference objects performs a given trafficmaneuver could include the statistically median path (i.e., drivingpath, for reference vehicles, biking path, for reference bicycles, andwalking path, for reference pedestrians and reference animals), speed,acceleration and orientation of the like population of reference objectsalong roadways, for instance, associated with its performance of thetraffic maneuver. The predominating attributes of how the likepopulation of reference objects performs the traffic maneuver couldmoreover include the statistically median lane offsets, thestatistically median proximity to obstacles on roadways and thestatistically median approach to obstacles on roadways, for instance,associated with its performance of the traffic maneuver.

On the other hand, the atypical attributes of how the like population ofreference objects performs a given traffic maneuver could include thestatistically outlying path (i.e., driving path, for reference vehicles,biking path, for reference bicycles, and walking path, for referencepedestrians and reference animals), speed, acceleration and orientationof the like population of reference objects along roadways, forinstance, associated with its performance of the traffic maneuver. Theatypical attributes of how the like population of reference objectsperforms the traffic maneuver could moreover include the statisticallyoutlying lane offsets, the statistically outlying proximity to obstacleson roadways and the statistically outlying approach to obstacles onroadways, for instance, associated with its performance of the trafficmaneuver.

Any given statistically median or otherwise predominating attribute ofhow the like population of reference objects performs a traffic maneuvermay be expressed in the traffic behavior model, in whole or in part, asa value, multiple values, a range of values and the like, for instance.Matching a given statistically median or otherwise predominatingattribute of how the like population of reference objects performs atraffic maneuver could, for instance, involve a corresponding valuebeing the same as or otherwise substantially similar to a value or oneof multiple values, or being within or substantially within a range ofvalues, as the case may be, expressed in the traffic behavior model.

Similarly, any given statistically outlying or otherwise atypicalattribute of how the like population of reference objects performs atraffic maneuver may be expressed in the traffic behavior model, inwhole or in part, as a value, multiple values, a range of values and thelike, for instance. Matching a given statistically outlying or otherwiseatypical attribute of how the like population of reference objectsperforms a traffic maneuver could, for instance, involve a correspondingvalue being the same as or otherwise substantially similar to a value orone of multiple values, or being within or substantially within a rangeof values, as the case may be, expressed in the traffic behavior model.

The traffic behavior model is received at the vehicle 10 in operation312. If operations 304-310 are performed in whole or in part off boardthe vehicle 10, the traffic behavior model is transmitted to the vehicle10 in operation 312. In onboard implementations of the process 300,where the process 300 is performed onboard the vehicle 10, operation 312may be inherent in the remainder of the process 300 or in otherprocesses when the traffic behavior model is ultimately received at theplanning/decision making module 94.

Providing User Assistance Based on Traffic Behavior Models

The vehicle 10, informed by traffic behavior models, is equipped toprovide a variety of assistance to a user of the vehicle 10. Processesfor providing user assistance are described with reference to thevehicle 10 as being a host vehicle equipped with the autonomousoperation system 20. Although the vehicle 10 is subject to autonomousoperation under the autonomous operation system 20, these processes mayalso leverage its components to provide user assistance that does notinclude autonomous operation of the vehicle 10.

Predicting the Future Maneuvering of Objects.

According to a process 400 shown in FIG. 4, the vehicle 10 and itsautonomous operation system 20 provide user assistance involvingpredicting the future maneuvering of objects in the environmentsurrounding the vehicle 10 by, among other ways, extrapolating thepredominating traffic behavior of like populations of reference objects,as described in traffic behavior models.

The process 400 is described with reference to FIG. 5. FIG. 5 shows anexample perspective view of the user of the vehicle 10 out of itswindshield 58, as well as conceptual renderings of outputs to the userat the various interfaces implemented by the components of theaudio/video system 46.

As shown in FIG. 5, the vehicle 10 is on a surface-type roadway 502, andis approaching an upcoming intersection 504 controlled by a trafficlight 506. The vehicle 10 is maneuvering along the roadway 502 accordingto a route for the vehicle 10 that dictates the vehicle 10 performing,among other driving maneuvers, current straight ahead driving toapproach the intersection 504, and an impending left-hand turn throughthe intersection 504. The route may, for example, be established by thenavigation system 44.

In operation 402, information about the vehicle 10 is detected by thesensor system 60 and its sensors, or is otherwise received, for examplefrom the V2V communication system 76 and digital maps, for gathering andevaluation by the perception module 92.

As the perception module 92 gathers and evaluates information about theoperation of the vehicle 10, it may identify the location and motion ofthe vehicle 10. As the perception module 92 gathers and evaluatesinformation about the environment surrounding the vehicle 10, it mayidentify, among other objects in the environment surrounding the vehicle10, the roadway 502 and its intersection 504, the traffic light 506, andobstacles on or around the roadway 502. These obstacles may includeother vehicles, such as a vehicle 510 flanking the vehicle 10 and avehicle 512 in front of the vehicle 10, as well as a bicycle 514 and apedestrian 516. The perception module 92 may further identify theproperties of these and other objects, including their presence,location and motion. For example, among other identifiable properties,the flanking vehicle 510 is moving in the same direction as the vehicle10 along the roadway 502, while the vehicle 512 is moving in theopposite direction as the vehicle 10 along the roadway 502 and is,accordingly, oncoming. The bicycle 514, similarly to the oncomingvehicle 512, is facing in the opposite direction as the vehicle 10 alongthe roadway 502, but is stationary. The pedestrian 516 is stationary aswell.

The roadway 502 has, among other identifiable features, lane markings.The lane markings mark the outside boundaries of the roadway 502 and theseparation of the roadway 502 into a middle section and surroundingsections for traffic moving in opposite directions. The middle sectionincludes a left-hand turn lane position 520 in which the vehicle 10 islocated, while the surrounding sections respectively include a laneposition 522 in which the flanking vehicle 510 is located, and anoncoming lane position 524 in which the oncoming vehicle 512 is located.The lane markings further mark a crossing lane position 526 for crossingtraffic, into which the vehicle 10 must enter as part of its performanceof the left-hand turn through the intersection 504. Both the bicycle 514and the pedestrian 516 are located off the roadway 502 (e.g., on asidewalk) across the intersection 504 from the vehicle 10. The bicycle514 is around the section of the roadway 502 for traffic moving in theopposite direction as the vehicle 10, while the pedestrian 516 is aroundthe section of the roadway 502 for traffic moving in the same directionas the vehicle 10. Among identifiable traffic rules, the traffic light506 dictates that, although the vehicle 10 may left-hand turn throughthe intersection 504, oncoming traffic, including the oncoming vehicle512 and the bicycle 514, has priority.

In operation 404, the information about the vehicle 10 is furtherevaluated by the perception module 92 to identify the traffic behaviorof one or more objects in the environment surrounding the vehicle 10.With the vehicle 10 maneuvering along the roadway 502 as shown in FIG.5, these objects in the environment surrounding the vehicle 10 couldinclude the oncoming vehicle 512, the bicycle 514 and the pedestrian516. The traffic behavior of the oncoming vehicle 512 is drivingbehavior, while the traffic behavior of the bicycle 514 is bikingbehavior, and the traffic behavior of the pedestrian 516 is walkingbehavior.

With the oncoming vehicle 512, the bicycle 514 and the pedestrian 516being objects in the environment surrounding the vehicle 10, as part ofoperation 404, the perception module 92 identifies one or more trafficmaneuvers being performed by the these objects. Generally speaking,among other information about the environment surrounding the vehicle10, the traffic maneuvers being performed by the objects are identifiedfrom their location and motion. The identification of the trafficmaneuvers being performed by the objects is informed by informationsourced from digital maps. This information includes the left-hand turnlane position 520 in which the vehicle 10 is located, the oncoming laneposition 524 in which the oncoming vehicle 512 is located, and thecrossing lane position 526, among other lane positions, as well astraffic rules including, among others, those dictated by the trafficlight 506. Additionally, or alternatively, this information could besourced from the vehicle 10. The identification of the traffic maneuversbeing performed by the objects is further informed by the location andmotion of other objects in the environment surrounding the vehicle 10,including other obstacles to them, such as the vehicle 10 itself. Withthe oncoming vehicle 512 maneuvering along the roadway 502 as shown inFIG. 5, a driving maneuver may, for instance, be its current straightahead driving to traverse the intersection 504. For the bicycle 514 offthe roadway 502, a biking maneuver may, for instance, be its currentstationary yielding to traffic in the intersection 504. Similarly, forthe pedestrian 516 off the roadway 502, a walking maneuver may, forinstance, be its current stationary yielding to traffic in theintersection 504.

Also as part of operation 404, the perception module 92 quantifiesattributes of how the objects in the environment surrounding the vehicle10 perform the traffic maneuvers. These correspond to statisticallymedian or otherwise predominating attributes of how like populations ofreference objects perform the traffic maneuvers, as well as thestatistically outlying or otherwise atypical attributes of how the likepopulations of reference objects perform the traffic maneuvers, asdescribed in respective traffic behavior models for the objectsgenerated according to the process 300. For the oncoming vehicle 512,the traffic behavior model describes the predominating driving behaviorand the atypical driving behavior of a like population of referencevehicles. For the bicycle 514, the traffic behavior model describes thepredominating biking behavior and the atypical biking behavior of a likepopulation of reference bicycles. For the pedestrian 516, the trafficbehavior model describes the predominating walking behavior and theatypical walking behavior of a like population of reference pedestrians.

In cases where, in the process 300, the information about the referenceobjects is sourced from the vehicle 10, operation 302 of the process 300may be performed in whole or in part in combination with operation 402,and operations 304 and 306 of the process 300 may be performed in wholeor in part in combination with operation 404. In these cases, thereference objects may include the flanking vehicle 510, the oncomingvehicle 512, the bicycle 514 and the pedestrian 516. In onboardimplementations of the process 300, where the process 300 is performedonboard the vehicle 10, operations 302-312 of the process 300 may beperformed, in real-time, in combination with operations 402 and 404,with the reference objects, once again, including the flanking vehicle510, the oncoming vehicle 512, the bicycle 514 and the pedestrian 516.

In operations 406-410, the planning/decision making module 94, based onthe evaluation of the information about the vehicle 10 by the perceptionmodule 92, predicts the future maneuvering of the oncoming vehicle 512,the bicycle 514 and the pedestrian 516 along the roadway 502, includingtheir paths (i.e., the driving path of the oncoming vehicle 512, thebiking path of the bicycle 514 and the walking path of the pedestrian516) along the roadway 502.

In operation 406, the planning/decision making module 94 identifieswhether the traffic behaviors of the oncoming vehicle 512, the bicycle514 and the pedestrian 516 are identifiable. Operation 406 may, forinstance, implement a confidence threshold that identifiability of thetraffic behaviors of the oncoming vehicle 512, the bicycle 514 and thepedestrian 516 must meet for purposes predicting their futuremaneuvering along the roadway 502 with a certain degree of confidence.

In both operations 408 and 410, the planning/decision making module 94predicts the future maneuvering of the oncoming vehicle 512, the bicycle514 and the pedestrian 516 along the roadway 502. If any of theirtraffic behaviors are not identifiable upon their initial identificationand classification, this is done in operation 408 by extrapolating thepredominating traffic behavior of the like populations of referenceobjects, as described in the respective traffic behavior models for theoncoming vehicle 512, the bicycle 514 and the pedestrian 516.

For instance, if the driving behavior of the oncoming vehicle 512 is notidentifiable, the planning/decision making module 94 predicts its futuremaneuvering along the roadway 502 by extrapolating the predominatingdriving behavior of a like population of reference vehicles, asdescribed in the traffic behavior model for the oncoming vehicle 512.More specifically, the predicted future maneuvering of the oncomingvehicle 512 along the roadway 502 includes one or more predicted drivingmaneuvers selected from among those performed by the like population ofreference vehicles. Moreover, for those predicted driving maneuvers, thepredicted future maneuvering of the oncoming vehicle 512 along theroadway 502 includes attributes of how the oncoming vehicle 512 ispredicted to perform the driving maneuvers that match correspondingstatistically median or otherwise predominating attributes of how thelike population of reference vehicles performs the predicted drivingmaneuvers.

Similarly, if its biking behavior is not identifiable, theplanning/decision making module 94 predicts the future maneuvering ofthe bicycle 514 along the roadway 502 by extrapolating the predominatingbiking behavior of a like population of reference bicycles, as describedin the traffic behavior model for the bicycle 514. The predicted futuremaneuvering of the bicycle 514 along the roadway 502 includes one ormore predicted biking maneuvers selected from among those performed bythe like population of reference bicycles. Moreover, for those predictedbiking maneuvers, the predicted future maneuvering of the bicycle 514along the roadway 502 includes attributes of how the bicycle 514 ispredicted to perform the biking maneuvers that match correspondingstatistically median or otherwise predominating attributes of how thelike population of reference bicycles performs the predicted bikingmaneuvers.

And, if its walking behavior is not identifiable, the planning/decisionmaking module 94 predicts the future maneuvering of the pedestrian 516along the roadway 502 by extrapolating the predominating walkingbehavior of a like population of reference pedestrians, as described inthe traffic behavior model for the pedestrian 516. The predicted futuremaneuvering of the pedestrian 516 along the roadway 502 includes one ormore predicted walking maneuvers selected from among those performed bythe like population of reference pedestrians. Moreover, for thosepredicted walking maneuvers, the predicted future maneuvering of thepedestrian 516 along the roadway 502 includes attributes of how thepedestrian 516 is predicted to perform the walking maneuvers that matchcorresponding statistically median or otherwise predominating attributesof how the like population of reference pedestrians performs thepredicted walking maneuvers.

If, on the other hand, any of their traffic behaviors are identifiable,the planning/decision making module 94 predicts the future maneuveringof the oncoming vehicle 512, the bicycle 514 and the pedestrian 516along the roadway 502 by extrapolating their respective trafficbehaviors in operation 410. For instance, if the driving behavior of theoncoming vehicle 512 is identifiable, the planning/decision makingmodule 94 predicts its future maneuvering along the roadway 502 byextrapolating its driving behavior. Accordingly, the predicted futuremaneuvering of the oncoming vehicle 512 along the roadway 502 includesone or more driving maneuvers being performed by the oncoming vehicle512, as well as attributes of how the oncoming vehicle 512 performs thedriving maneuvers.

Similarly, if the biking behavior of the bicycle 514 is identifiable,the planning/decision making module 94 predicts its future maneuveringalong the roadway 502 by extrapolating its biking behavior. Accordingly,the predicted future maneuvering of the bicycle 514 along the roadway502 includes one or more biking maneuvers being performed by the bicycle514, as well as attributes of how the bicycle 514 performs the bikingmaneuvers.

And, if the walking behavior of the pedestrian 516 is identifiable, theplanning/decision making module 94 predicts its future maneuvering alongthe roadway 502 by extrapolating its walking behavior. Accordingly, thepredicted future maneuvering of the pedestrian 516 along the roadway 502includes one or more walking maneuvers being performed by the pedestrian516, as well as attributes of how the pedestrian 516 performs thewalking maneuvers.

Regardless of whether any of their traffic behaviors are identifiableupon their initial identification and classification, in operation 412,the vehicle 10 alerts the user of the predicted future maneuvering ofthe oncoming vehicle 512, the bicycle 514 and the pedestrian 516 alongthe roadway 502, including the driving path of the oncoming vehicle 512,the biking path of the bicycle 514 and the walking path of thepedestrian 516 along the roadway 502, if applicable.

As shown in FIG. 5, the alerts of the predicted future maneuvering ofthe oncoming vehicle 512, the bicycle 514 and the pedestrian 516 alongthe roadway 502 are issued to the user as outputs 530 at the surface ofthe windshield 58. Accordingly, the planning/decision making module 94may generate signals representing these things as media transformableinto visual outputs that may be projected onto the surface of thewindshield 58 by the projector 56 of the audio/video system 46. Althoughthese things are described with reference to the outputs 530 at thesurface of the windshield 58, additionally, or alternatively, they couldsimilarly be issued to the user as outputs 530 at the interfacesimplemented by the other components of the audio/video system 46, suchas its displays 54 and its speakers 52.

As shown with reference to outputs 530 a-c, in non-identifiablescenarios, the alerts of the predicted future maneuvering of theoncoming vehicle 512, the bicycle 514 and the pedestrian 516 along theroadway 502 include various notifications. For the oncoming vehicle 512,these include notifications of one or more driving maneuvers selectedfrom among those performed by the like population of reference vehicles,as well as concurrent notifications of attributes of how the oncomingvehicle 512 is predicted to perform the driving maneuvers that matchcorresponding statistically median or otherwise predominating attributesof how the like population of reference vehicles performs the drivingmaneuvers. Similarly, for the bicycle 514, these include notificationsof one or more biking maneuvers selected from among those performed bythe like population of reference bicycles, as well as concurrentnotifications of attributes of how the bicycle 514 is predicted toperform the biking maneuvers that match corresponding statisticallymedian or otherwise predominating attributes of how the like populationof reference bicycles performs the biking maneuvers. And, for thepedestrian 516, these include notifications of one or more walkingmaneuvers selected from among those performed by the like population ofreference pedestrians, as well as concurrent notifications of attributesof how the pedestrian 516 is predicted to perform the walking maneuversthat match corresponding statistically median or otherwise predominatingattributes of how the like population of reference pedestrians performsthe walking maneuvers.

With the oncoming vehicle 512 being initially identified as located inthe oncoming lane position 524, and classified as a vehicle, as shownwith reference to the output 530 a, an alert of the predicted futuremaneuvering of the oncoming vehicle 512 along the roadway 502 mayinclude notifications that straight ahead driving to traverse theintersection 504 is a predicted driving maneuver selected from amongthose performed by the like population of reference vehicles, and thatthe predominating speed of the like population of reference vehiclesalong roadways associated with its performance of straight ahead drivingto traverse intersections is thirty-five miles per hour. With thebicycle 514 being initially identified as located off the roadway 502and facing in the opposite direction as the vehicle 10, and classifiedas a bicycle, as shown with reference to the output 530 b, an alert ofthe predicted future maneuvering of the bicycle 514 along the roadway502 may include notifications that straight ahead biking to traverse theintersection 504 is a predicted biking maneuver selected from amongthose performed by the like population of reference bicycles, and thatthe predominating speed of the like population of reference bicyclesalong roadways associated with its performance of straight ahead bikingto traverse intersections is ten miles per hour. Concurrentnotifications may be included of the driving path of the oncomingvehicle 512 along the roadway 502 included in the predicted futuremaneuvering of the oncoming vehicle 512, and of the biking path of thebicycle 514 along the roadway 502 included in the predicted futuremaneuvering of the bicycle 514. With the pedestrian 516 being initiallyidentified as located off the roadway 502, and classified as apedestrian, as shown with reference to the output 530 c, an alert of thepredicted future maneuvering of the pedestrian 516 along the roadway 502may include notifications that stationary yielding to traffic in theintersection 504 is a predicted walking maneuver selected from amongthose performed by the like population of reference pedestrians. In thiscase, the walking path of the pedestrian 516 along the roadway 502 isinapplicable.

As shown with reference to outputs 530 d-f, in identifiable scenarios,the alerts of the predicted future maneuvering of the oncoming vehicle512, the bicycle 514 and the pedestrian 516 along the roadway 502include various notifications analogous to those in non-identifiablescenarios. For the oncoming vehicle 512, these include notifications ofone or more driving maneuvers being performed by the oncoming vehicle512, as well as attributes of how the oncoming vehicle 512 performs thedriving maneuvers. Similarly, for the bicycle 514, these includenotifications of one or more biking maneuvers being performed by thebicycle 514, as well as attributes of how the bicycle 514 performs thebiking maneuvers. And, for the pedestrian 516, these includenotifications of one or more walking maneuvers being performed by thepedestrian 516, as well as attributes of how the pedestrian 516 performsthe walking maneuvers.

With the current straight ahead driving to traverse the intersection 504being a driving maneuver being performed by the oncoming vehicle 512, asshown with reference to the output 530 d, an alert of the predictedfuture maneuvering of the oncoming vehicle 512 along the roadway 502 mayinclude notifications that the oncoming vehicle 512 is predicted toconsummate the current straight ahead driving to traverse theintersection 504, and that speed of the oncoming vehicle 512 along theroadway 502 associated with its performance of the current straightahead driving to traverse the intersection 504 is forty miles per hour.Once again, a concurrent notification may be included of the drivingpath of the oncoming vehicle 512 along the roadway 502 included in thepredicted future maneuvering of the oncoming vehicle 512. As shown withreference to the output 530 e and the output 530 f, alerts of thepredicted future maneuvering of the bicycle 514 and the pedestrian 516along the roadway 502 may include notifications of their currentstationary yielding to traffic in the intersection 504. In these cases,the biking path of the bicycle 514 along the roadway 502, and thewalking path of the pedestrian 516 along the roadway 502, areinapplicable.

Matching Autonomous Operation.

According to a process 600 shown in FIG. 6, the vehicle 10 and itsautonomous operation system 20 provide user assistance by initiating,maintaining or otherwise implementing autonomous operation of thevehicle 10 under which its driving behavior matches the predominatingdriving behavior of a like population of reference vehicles, asdescribed in a traffic behavior model.

In operation 602, information about the vehicle 10 is detected by thesensor system 60 and its sensors, or is otherwise received, for examplefrom the V2V communication system 76 and digital maps, for gathering andevaluation by the perception module 92.

In the case of information about the environment surrounding the vehicle10, the perception module 92 may, as part of its evaluation, identify,among other objects in the environment surrounding the vehicle 10,roadways, as well as any obstacles on or around the roadways, such asother vehicles. In addition to identifying roadways themselves, theperception module 92 may identify their features, such as lane markings,as well as different lane positions. In addition to identifyingobstacles themselves, the perception module 92 may identify theirproperties, such as their presence, location and motion.

In cases where, in the process 300, the information about the referenceobjects is sourced from the vehicle 10, operation 302 of the process 300may be performed in whole or in part in combination with operation 602.In these cases, the reference objects may include the identified objectsin the environment surrounding the vehicle 10. In onboardimplementations of the process 300, where the process 300 is performedonboard the vehicle 10, operations 302-312 of the process 300 may beperformed, in real-time, in combination with operation 602, with thereference objects, once again, including the identified objects in theenvironment surrounding the vehicle 10.

In operation 604, the planning/decision making module 94 generates adriving plan under which the driving behavior of the vehicle 10 matchesthe predominating driving behavior of a like population of referencevehicles, as described in a traffic behavior model for the vehicle 10generated according to the process 300. The driving plan is generatedbased on the traffic behavior model, as well as the evaluation of theinformation about the vehicle 10 by the perception module 92.

The driving plan is for performing a driving maneuver, which may includeany number of sub-driving maneuvers. In order to match the predominatingdriving behavior, the driving plan is, more specifically, for performinga driving maneuver performed by the like population of referencevehicles, as described in the traffic behavior model. The drivingmaneuver may be selected from among the driving maneuvers performed bythe like population of reference vehicles, for example, or couldinitially be identified as a candidate and confirmed as being among thedriving maneuvers performed by the like population of referencevehicles. In any event, the driving maneuver may be either dictated by aroute for the vehicle 10 established by the navigation system 44 oridentified based on the evaluation of the information about the vehicle10 by the perception module 92, or both.

The driving plan describes various things about performing its drivingmaneuver. These things correspond to the attributes of how the likepopulation of reference vehicles performs the driving maneuver. In orderto match the predominating driving behavior, one, some or all of thesethings, as described in the driving plan, match the statistically medianor otherwise predominating corresponding attributes of how the likepopulation of reference vehicles performs the driving maneuver, asdescribed in the traffic behavior model.

Among other things, the driving plan describes the motion of the vehicle10 along a roadway. Accordingly, part of the driving plan may describe adriving path of the vehicle 10 along a roadway, for instance, thatmatches the predominating driving path of the like population ofreference vehicles along roadways associated with its performance of thedriving maneuver. Other parts the driving plan may describe a speed,acceleration and orientation of the vehicle 10 along the roadway, forinstance, that match the predominating speed, acceleration andorientation, as the case may be, of the like population of referencevehicles along roadways associated with its performance of the drivingmaneuver.

The driving plan is also generated based on the information about theenvironment surrounding the vehicle 10. The driving plan accounts fordifferent lane positions and traffic rules and, accordingly, maydescribe a lane offset, for instance, that matches the predominatinglane offset associated with the performance of the driving maneuver bythe like population of reference vehicles. The driving plan alsoaccounts for any objects in the environment surrounding the vehicle 10,as well as their properties. In the case of obstacles on the roadway,the driving plan may accordingly describe a proximity to obstacles onthe roadway and an approach to obstacles on the roadway, for instance,that match the predominating proximity to obstacles on roadways and thepredominating approach to obstacles on roadways, as the case may be,associated with the performance of the driving maneuver by the likepopulation of reference vehicles.

Additionally, for any objects in the environment surrounding the vehicle10, and particularly in the case of obstacles on or around the roadway,the driving plan may account for their predicted future maneuveringalong the roadway, as predicted according to the process 400. Thepredicted future maneuvering of an obstacle along the roadway maydescribe, similarly to a driving plan, the motion of the obstacle alongthe roadway, including the path of the obstacle along the roadway, aswell as the speed, acceleration and orientation of the obstacle alongthe roadway.

Upon the planning/decision making module 94 generating the driving planin operation 604, in operation 606, the control module 96 operates theappropriate vehicle systems 30 to execute the driving plan. With theexecution of the driving plan, the vehicle 10 is maneuvered according tothe driving plan with a driving behavior that matches the predominatingdriving behavior.

Training for Matching Manual Operation.

According to a process 700 shown in FIG. 7, the vehicle 10 and itsautonomous operation system 20 provide user assistance by activelytraining a user to implement manual operation of the vehicle 10 underwhich its driving behavior matches the predominating driving behavior ofa like population of reference vehicles, as described in a trafficbehavior model.

The process 700 is described with reference to FIGS. 8A and 8B. FIG. 8Ashows an example perspective view of the user of the vehicle 10 out ofits windshield 58. Both FIG. 8A and FIG. 8B show conceptual renderingsof outputs to the user at the various interfaces implemented by thecomponents of the audio/video system 46.

As shown in FIG. 8A, the vehicle 10 is on a surface-type roadway 802,and is approaching an upcoming intersection 804 controlled by a trafficlight 806. The vehicle 10 is maneuvering along the roadway 802 accordingto a route for the vehicle 10 that dictates the vehicle 10 performing,among other driving maneuvers, current straight ahead driving toapproach the intersection 804, and an impending left-hand turn throughthe intersection 804. The route may, for example, be established by thenavigation system 44.

In operation 702, information about the vehicle 10 is detected by thesensor system 60 and its sensors, or is otherwise received, for examplefrom the V2V communication system 76 and digital maps, for gathering andevaluation by the perception module 92.

As the perception module 92 gathers and evaluates information about theoperation of the vehicle 10, it may identify the location and motion ofthe vehicle 10. As the perception module 92 gathers and evaluatesinformation about the environment surrounding the vehicle 10, it mayidentify, among other objects in the environment surrounding the vehicle10, the roadway 802 and its intersection 804, the traffic light 806, andobstacles on or around the roadway 802. These obstacles may includeother vehicles, such as a vehicle 810 flanking the vehicle 10 and avehicle 812 in front of the vehicle 10, as well as a bicycle 814 and apedestrian 816. The perception module 92 may further identify theproperties of these and other objects, including their presence,location and motion. For example, among other identifiable properties,the flanking vehicle 810 is moving in the same direction as the vehicle10 along the roadway 802, while the vehicle 812 is moving in theopposite direction as the vehicle 10 along the roadway 802 and is,accordingly, oncoming. The bicycle 814, similarly to the oncomingvehicle 812, is facing in the opposite direction as the vehicle 10 alongthe roadway 802, but is stationary. The pedestrian 816 is stationary aswell.

The roadway 802 has, among other identifiable features, lane markings.The lane markings mark the outside boundaries of the roadway 802 and theseparation of the roadway 802 into a middle section and surroundingsections for traffic moving in opposite directions. The middle sectionincludes a left-hand turn lane position 820 in which the vehicle 10 islocated, while the surrounding sections respectively include a laneposition 822 in which the flanking vehicle 810 is located, and anoncoming lane position 824 in which the oncoming vehicle 812 is located.The lane markings further mark a crossing lane position 826 for crossingtraffic, into which the vehicle 10 must enter as part of its performanceof the left-hand turn through the intersection 804. Both the bicycle 814and the pedestrian 816 are located off the roadway 802 (e.g., on asidewalk) across the intersection 804 from the vehicle 10. The bicycle814 is around the section of the roadway 802 for traffic moving in theopposite direction as the vehicle 10, while pedestrian 816 is around thesection of the roadway 802 for traffic moving in the same direction asthe vehicle 10. Among identifiable traffic rules, the traffic light 806dictates that, although the vehicle 10 may left-hand turn through theintersection 804, oncoming traffic, including the oncoming vehicle 812and the bicycle 514, has priority.

In cases where, in the process 300, the information about the referenceobjects is sourced from the vehicle 10, operation 302 of the process 300may be performed in whole or in part in combination with operation 702.In these cases, the reference objects may include the flanking vehicle810, the oncoming vehicle 812, the bicycle 814 and the pedestrian 816.In onboard implementations of the process 300, where the process 300 isperformed onboard the vehicle 10, operations 302-312 of the process 300may be performed, in real-time, in combination with operation 702, withthe reference objects, once again, including the flanking vehicle 810,the oncoming vehicle 812, the bicycle 816 and the pedestrian 816.

In operation 704, while the vehicle 10 is in the midst of manualoperation, the vehicle 10 prospectively instructs the user how to makethe driving behavior of the vehicle 10 match the predominating drivingbehavior of a like population of reference vehicles, as described in atraffic behavior model for the vehicle 10 generated according to theprocess 300.

As shown in FIG. 8A, the prospective instructions are issued to the useras outputs 830 at the surface of the windshield 58. Accordingly, theplanning/decision making module 94 may generate signals representing theprospective instructions as media transformable into visual outputs thatmay be projected onto the surface of the windshield 58 by the projector56 of the audio/video system 46. Although the prospective instructionsare described with reference to the outputs 830 at the surface of thewindshield 58, additionally, or alternatively, they could similarly beissued to the user as outputs 830 at the interfaces implemented by theother components of the audio/video system 46, such as its displays 54and its speakers 52.

As part of operation 704, the planning/decision making module 94identifies an impending driving maneuver as a training driving maneuver.In order to match the predominating driving behavior, the impendingtraining driving maneuver is one performed by the like population ofreference vehicles, as described in the traffic behavior model. Theimpending training driving maneuver may be selected from among thedriving maneuvers performed by the like population of referencevehicles, for example, or could initially be identified as a candidateand confirmed as being among the driving maneuvers performed by the likepopulation of reference vehicles. In any event, the impending trainingdriving maneuver may be either dictated by the route for the vehicle 10established by the navigation system 44 or identified based on theevaluation of the information about the vehicle 10 by the perceptionmodule 92, or both. With the vehicle 10 maneuvering along the roadway802 as shown in FIG. 8A, the impending training driving maneuver may,for instance, be the impending left-hand turn through the intersection804.

As shown with reference to an output 830 a, the prospective instructionsinclude a notification of the impending training driving maneuver.Additionally, as shown with reference to outputs 830 b-d, theprospective instructions include concurrent notifications of one, someor all of the statistically median or otherwise predominating attributesof how the like population of reference vehicles performs the impendingtraining driving maneuver, as described in the traffic behavior model.

With the impending left-hand turn through the intersection 804 being thetraining driving maneuver, as shown with reference to the output 830 a,the prospective instructions include a notification of the left-handturn through the intersection 804. As shown with reference to the output830 b, the prospective instructions may include a notification that thepredominating driving path of the like population of reference vehiclesalong roadways associated with its performance of left-hand turnsthrough intersections is centered. As shown with reference to the output830 c, the prospective instructions may further include a notificationthat the predominating speed of the like population of referencevehicles along roadways associated with its performance of left-handturns through intersections is fifteen miles per hour. As shown withreference to the output 830 d, the prospective instructions may alsoinclude a notification that the predominating proximity to oncomingvehicles on roadways associated with the performance of left-hand turnsthrough intersections by the like population of reference vehicles isbetween ten and fifteen car lengths. The prospective instructions couldalso include any combination of analogous notifications of otherpredominating attributes of how the like population of referencevehicles performs the training driving maneuver.

According to the prospective instructions, the user is notified that, inorder to make the driving behavior of the vehicle 10 match thepredominating driving behavior, the user should implement manualoperation of the vehicle 10 under which the left-hand turn through theintersection 804 is performed. The prospective instructions furthernotify the user that, under the manual operation of the vehicle 10, itshould have a driving path along the roadway 802 that stays centered inthe left-hand turn lane position 820 and the lane position 826 forcrossing traffic, should have a speed of fifteen miles per hour alongthe roadway 802, and should maintain a proximity to the oncoming vehicle812 on the roadway 802 between ten and fifteen car lengths.

While the vehicle 10 presumably performs the training driving maneuverin the midst of manual operation, information about the vehicle 10 isgathered for evaluation by the perception module 92, in a continuationof operation 702. In operation 706, this information is evaluated by theperception module 92 to identify the driving behavior of the vehicle 10.

As part of operation 706, the perception module 92 identifies an actualdriving maneuver performed by the vehicle 10. Generally speaking, amongother information about the vehicle 10, the actual driving maneuverperformed by the vehicle 10 is identified from its location and motion,as well as from information about the environment surrounding thevehicle 10. In the case of information about the environment surroundingthe vehicle 10, the identification of the actual driving maneuverperformed by the vehicle 10 is informed by information sourced fromdigital maps. This information includes the left-hand turn lane position820 in which the vehicle 10 is located, the oncoming lane position 824and the crossing lane position 826, among other lane positions, as wellas traffic rules including, among others, those dictated by the trafficlight 806. Additionally, or alternatively, this information could besourced from the vehicle 10. The identification of the actual drivingmaneuver performed by the vehicle 10 is further informed by the locationand motion of objects in the environment surrounding the vehicle 10,including obstacles such as the oncoming vehicle 812, the bicycle 814and the pedestrian 816.

Also as part of operation 706, the perception module 92 quantifiesattributes of how the vehicle 10 performs the actual driving maneuver.These include, at least, those corresponding to the notifiedpredominating attributes of how the like population of referencevehicles performs the training driving maneuver. Accordingly, for theprospective instructions included for the left-hand turn through theintersection 804, the attributes of how the vehicle 10 performs theactual driving maneuver include the driving path of the vehicle 10 alongthe roadway 802, the speed of the vehicle 10 along the roadway 802 andthe proximity to objects on the roadway 802 associated with itsperformance of the actual driving maneuver. The attributes of how thevehicle 10 performs the actual driving maneuver could further includethose corresponding to any combination of other predominating attributesof how the like population of reference vehicles performs the trainingdriving maneuver.

In operation 708, the planning/decision making module 94 identifieswhether the driving behavior of the vehicle 10 matches the predominatingdriving behavior. As part of operation 708, the planning/decision makingmodule 94 identifies whether the actual driving maneuver performed bythe vehicle 10 is the same as the training driving maneuver. Also aspart of operation 708, the planning/decision making module 94identifies, on an attribute-by-attribute basis, whether the attributesof how the vehicle 10 performed the actual driving maneuver match thecorresponding notified predominating attributes of how the likepopulation of reference vehicles performs the training driving maneuver.For any non-matching attributes of how the vehicle 10 performed theactual driving maneuver, the planning/decision making module 94 may alsoidentify whether they match corresponding atypical attributes of how thelike population of reference vehicles performs the training drivingmaneuver.

If the driving behavior of the vehicle 10 matches the predominatingdriving behavior in all respects, the vehicle 10 confirms this to theuser in operation 710. On the other hand, if the driving behavior of thevehicle 10 does not match the predominating driving behavior in anyrespect, the vehicle 10, in operation 712, remedially instructs the userhow to make the driving behavior of the vehicle 10 match thepredominating driving behavior.

As shown in FIG. 8B, both the confirmation and the remedial instructionsare issued to the user as outputs 840 at the surface of a representativedisplay 54 of the audio/video system 46. Accordingly, theplanning/decision making module 94 may generate signals representingthese things as media transformable into visual outputs at the surfacesof the displays 54 of the audio/video system 46. Although these thingsare described with reference to the outputs 840 at the surface of thedisplays 54, additionally, or alternatively, they could similarly beissued to the user as outputs 840 at the interfaces implemented by theother components of the audio/video system 46, such as its projector 56and its speakers 52.

As shown with reference to an output 840 a, both the confirmation andremedial instructions include a notification of whether the actualdriving maneuver performed by the vehicle 10 is the same as the trainingdriving maneuver. Assuming this is the case, as shown with reference tooutputs 840 b-d, both the confirmation and the remedial instructionsadditionally include concurrent notifications, on anattribute-by-attribute basis, of whether the attributes of how thevehicle 10 performed the actual driving maneuver match the correspondingnotified predominating attributes of how the like population ofreference vehicles performs the training driving maneuver.

The outputs 840, as a whole, reflect the case where the driving behaviorof the vehicle 10 matches the predominating driving behavior in some butnot all respects. The outputs 840 accordingly represent variousnotifications included in the remedial instructions. In the case wherethe driving behavior of the vehicle 10 matches the predominating drivingbehavior in all respects, the same or similar outputs 840 couldrepresent analogous notifications included in the confirmation.

With the left-hand turn through the intersection 804 being the trainingdriving maneuver, and with the actual driving maneuver performed by thevehicle 10 being the same as the training driving maneuver, the remedialinstructions include a notification that the actual driving maneuverperformed by the vehicle 10 is the same as the training drivingmaneuver, as shown with reference to the output 840 a. As shown withreference to the output 840 b, the remedial instructions may include anotification that the driving path of the vehicle 10 along the roadway802 associated with its performance of the left-hand turn through theintersection 804 was centered and, accordingly, matches thepredominating driving path of the like population of reference vehiclesalong roadways associated with its performance of left-hand turnsthrough intersections. As shown with reference to the output 840 c, theremedial instructions may further include a notification that the speedof the vehicle 10 along the roadway 802 associated with its performanceof the left-hand turn through the intersection 804 was fifteen miles perhour and, accordingly, matches the predominating speed of the likepopulation of reference vehicles along roadways associated with itsperformance of left-hand turns through intersections. On the other hand,as shown with reference to the output 840 d, the remedial instructionsmay also include a notification that the proximity to the oncomingvehicle 812 on the roadway 802 associated with its performance of theleft-hand turn through the intersection 804 was seven car lengths and,accordingly, does not match the predominating proximity to oncomingvehicles on roadways associated with the performance of left-hand turnsthrough intersections by the like population of reference vehicles. Theremedial instructions could also include any combination of analogousnotifications of attributes of how the vehicle 10 performed the actualdriving maneuver corresponding to other predominating attributes of howthe like population of reference vehicles performs the training drivingmaneuver.

According to the remedial instructions, the user is notified that itcorrectly implemented manual operation of the vehicle 10 under which theleft-hand turn through the intersection 804 was performed. The remedialinstructions further notify the user that, under the manual operation ofthe vehicle 10, it correctly had a driving path along the roadway 802that stayed centered in the left-hand turn lane position 820 and thelane position 826 for crossing traffic, and correctly had a speed offifteen miles per hour along the roadway 802. The remedial instructionsalso notify the user that, on the other hand, under the manual operationof the vehicle 10, it should have maintained a proximity to the oncomingvehicle 812 on the roadway 802 between ten and fifteen car lengths.

Prompting Corrective Matching Manual or Autonomous Operation.

According to a process 900 shown in FIG. 9, the vehicle 10 and itsautonomous operation system 20 provide user assistance by promptingcorrective manual or autonomous operation of the vehicle 10 under whichits driving behavior matches the predominating driving behavior of alike population of reference vehicles, as described in a trafficbehavior model.

The process 900 is described with reference to FIG. 11. FIG. 11 shows anexample perspective view of the user of the vehicle 10 out of itswindshield 58. FIG. 11 further shows conceptual renderings of outputs tothe user at the various interfaces implemented by the components of theaudio/video system 46.

As shown in FIG. 11, the vehicle 10 is on a surface-type roadway 1102,and is approaching an upcoming intersection 1104 controlled by a trafficlight 1106. The vehicle 10 is maneuvering along the roadway 1102according to a route for the vehicle 10 that dictates the vehicle 10performing, among other driving maneuvers, current straight aheaddriving to approach the intersection 1104, and an impending left-handturn through the intersection 1104. The route may, for example, beestablished by the navigation system 44.

In operation 902, information about the vehicle 10 is detected by thesensor system 60 and its sensors, or is otherwise received, for examplefrom the V2V communication system 76 and digital maps, for gathering andevaluation by the perception module 92.

As the perception module 92 gathers and evaluates information about theoperation of the vehicle 10, it may identify the location and motion ofthe vehicle 10. As the perception module 92 gathers and evaluatesinformation about the environment surrounding the vehicle 10, it mayidentify, among other objects in the environment surrounding the vehicle10, the roadway 1102 and its intersection 1104, the traffic light 1106,and obstacles on or around the roadway 1102. These obstacles may includeother vehicles, such as a vehicle 1110 flanking the vehicle 10 and avehicle 1112 in front of the vehicle 10, as well as a bicycle 1114 and apedestrian 1116. The perception module 92 may further identify theproperties of these and other objects, including their presence,location and motion. For example, among other identifiable properties,the flanking vehicle 1110 is moving in the same direction as the vehicle10 along the roadway 1102, while the vehicle 1112 is moving in theopposite direction as the vehicle 10 along the roadway 1102 and is,accordingly, oncoming. The bicycle 1114, similarly to the oncomingvehicle 1112, is facing in the opposite direction as the vehicle 10along the roadway 1102, but is stationary. The pedestrian 1116 isstationary as well.

The roadway 1102 has, among other identifiable features, lane markings.The lane markings mark the outside boundaries of the roadway 1102 andthe separation of the roadway 1102 into a middle section and surroundingsections for traffic moving in opposite directions. The middle sectionincludes a left-hand turn lane position 1120 in which the vehicle 10 islocated, while the surrounding sections respectively include a laneposition 1122 in which the flanking vehicle 1110 is located, and anoncoming lane position 1124 in which the oncoming vehicle 1112 islocated. The lane markings further mark a crossing lane position 1126for crossing traffic, into which the vehicle 10 must enter as part ofits performance of the left-hand turn through the intersection 1104.Both the bicycle 1114 and the pedestrian 1116 are located off theroadway 1102 (e.g., on a sidewalk) across the intersection 1104 from thevehicle 10. The bicycle 1114 is around the section of the roadway 1102for traffic moving in the opposite direction as the vehicle 10, whilepedestrian 1116 is around the section of the roadway 1102 for trafficmoving in the same direction as the vehicle 10. Among identifiabletraffic rules, the traffic light 1106 dictates that, although thevehicle 10 may left-hand turn through the intersection 1104, oncomingtraffic, including the oncoming vehicle 1112 and the bicycle 1114, haspriority.

In cases where, in the process 300, the information about the referenceobjects is sourced from the vehicle 10, operation 302 of the process 300may be performed in whole or in part in combination with operation 902.In these cases, the reference objects may include the flanking vehicle1110, the oncoming vehicle 1112, the bicycle 1114 and the pedestrian1116. In onboard implementations of the process 300, where the process300 is performed onboard the vehicle 10, operations 302-312 of theprocess 300 may be performed, in real-time, in combination withoperation 902, with the reference objects, once again, including theflanking vehicle 1110, the oncoming vehicle 1112, the bicycle 1114 andthe pedestrian 1116.

In operation 904, while the vehicle 10 is in the midst of manualoperation, the information about the vehicle 10 is further evaluated bythe perception module 92 to identify the driving behavior of the vehicle10.

As part of operation 904, the perception module 92 identifies one ormore actual driving maneuvers being performed by the vehicle 10.Generally speaking, among other information about the vehicle 10, theactual driving maneuvers being performed by the vehicle 10 areidentified from its location and motion, as well as from informationabout the environment surrounding the vehicle 10. In the case ofinformation about the environment surrounding the vehicle 10, theidentification of the actual driving maneuvers being performed by thevehicle 10 is informed by information sourced from digital maps. Thisinformation includes the left-hand turn lane position 1120 in which thevehicle 10 is located, the oncoming lane position 1124 and the crossinglane position 1126, among other lane positions, as well as traffic rulesincluding, among others, those dictated by the traffic light 1106.Additionally, or alternatively, this information could be sourced fromthe vehicle 10. The identification of the actual driving maneuvers beingperformed by the vehicle 10 is further informed by the location andmotion of objects in the environment surrounding the vehicle 10,including obstacles such as the oncoming vehicle 1112, the bicycle 1114and the pedestrian 1116. With the vehicle 10 maneuvering along theroadway 1102 as shown in FIG. 11, an actual driving maneuver may, forinstance, be the current straight ahead driving to approach theintersection 1104.

Also as part of operation 904, the perception module 92 quantifiesattributes of how the vehicle 10 performs the actual driving maneuvers.These correspond to statistically median or otherwise predominatingattributes of how the like population of reference vehicles performs theactual driving maneuvers, as well as the statistically outlying orotherwise atypical attributes of how the like population of referencevehicles performs the actual driving maneuvers, as described in atraffic behavior model for the vehicle 10 generated according to theprocess 300.

In operation 906, the planning/decision making module 94 identifieswhether the driving behavior of the vehicle 10 matches the predominatingdriving behavior. As part of operation 906, the planning/decision makingmodule 94 identifies whether actual driving maneuvers being performed bythe vehicle 10 are the same as the driving maneuvers performed by thelike population of reference vehicles, as described in the trafficbehavior model. Also as part of operation 906, the planning/decisionmaking module 94 identifies, on an attribute-by-attribute basis, whetherthe attributes of how the vehicle 10 performs the actual drivingmaneuvers match corresponding predominating attributes of how the likepopulation of reference vehicles performs the driving maneuvers. For anynon-matching attributes of how the vehicle 10 performs an actual drivingmaneuver, the planning/decision making module 94 may also identifywhether they match corresponding atypical attributes of how the likepopulation of reference vehicles performs the driving maneuver.

If the driving behavior of the vehicle 10 matches the predominatingdriving behavior in all respects, the process 900 returns to operation902. On the other hand, if the driving behavior of the vehicle 10 isatypical of the predominating driving behavior, or otherwise does notmatch the predominating driving behavior, in any respect, the vehicle10, in operations 908-912, prompts the user to implement correctivemanual operation of the vehicle 10 under which its driving behaviormatches the predominating driving behavior of the like population ofreference vehicles.

To prompt the user to implement corrective manual operation of thevehicle 10 if its driving behavior is atypical of the predominatingdriving behavior, the vehicle 10 warns or otherwise alerts the user ofthis in operation 908. Similarly, in operation 910, if the drivingbehavior of the vehicle 10 is not atypical of the predominating drivingbehavior, but otherwise does not match the predominating drivingbehavior in any respect, the vehicle 10 alerts the user of this toprompt the user to implement corrective operation of the vehicle 10. Tofurther prompt the user to implement corrective operation of the vehicle10 in either case, optionally, in operation 912, the vehicle 10 mayremedially instruct the user how to make the driving behavior of thevehicle 10 match the predominating driving behavior.

As shown in FIG. 11, the alerts and remedial instructions prompting theuser to implement corrective manual operation of the vehicle 10 areissued to the user as outputs 1130 at the surface of the windshield 58.Accordingly, the planning/decision making module 94 may generate signalsrepresenting these things as media transformable into visual outputsthat may be projected onto the surface of the windshield 58 by theprojector 56 of the audio/video system 46. Although these things aredescribed with reference to the outputs 1130 at the surface of thewindshield 58, additionally, or alternatively, they could similarly beissued to the user as outputs 1130 at the interfaces implemented by theother components of the audio/video system 46, such as its displays 54and its speakers 52.

Optionally, the alerts and remedial instructions prompting the user toimplement corrective manual operation of the vehicle 10 could include anotification of whether the actual driving maneuvers performed by thevehicle 10 are the same as the driving maneuvers performed by the likepopulation of reference vehicles. Assuming this is the case, as shownwith reference to outputs 1130 a and 1130 b, the alerts and remedialinstructions prompting the user to implement corrective manual operationof the vehicle 10 include notifications of one, some or all of theattributes of how the vehicle 10 performs the actual driving maneuvers.As additionally shown with reference to outputs 1130 a and 1130 b, thesethings also include concurrent notifications, on anattribute-by-attribute basis, that the attributes of how the vehicle 10performs the actual driving maneuvers match corresponding atypicalattributes of how the like population of reference vehicles performs thedriving maneuvers, or otherwise do not match corresponding predominatingattributes of how the like population of reference vehicles performs thedriving maneuvers.

With the current straight ahead driving to approach the intersection1104 being an actual driving maneuver, in an atypical scenario, as shownwith reference to the output 1130 a, the alert may include anotification that the speed of the vehicle 10 along the roadway 1102associated with its performance of the current straight ahead driving toapproach the intersection 1104 is two miles per hour and, accordingly,matches an atypical speed of the like population of reference vehiclesalong roadways associated with its performance of straight ahead drivingto approach intersections. In a non-predominating scenario, as shownwith reference to the output 1130 b, the alert may include anotification that the speed of the vehicle 10 along the roadway 1102associated with its performance of the current straight ahead driving toapproach the intersection 1104 is seven miles per hour and, accordingly,does not match the predominating speed of the like population ofreference vehicles along roadways associated with its performance ofstraight ahead driving to approach intersections. Regardless of thescenario, as shown with reference to both the output 1130 a and theoutput 1130 b, the remedial instructions may provide context about thepredominating driving behavior by including a concurrent notificationthat the predominating speed of the like population of referencevehicles along roadways associated with its performance of straightahead driving to approach intersections is ten miles per hour. Thealerts and remedial instructions prompting the user to implementcorrective manual operation of the vehicle 10 could also include anycombination of analogous notifications of attributes of how the vehicle10 performs the actual driving maneuvers corresponding to other atypicaland predominating attributes of how the like population of referencevehicles performs the driving maneuvers.

According to the alerts and remedial instructions prompting the user toimplement corrective manual operation of the vehicle 10, the user isnotified that it is not correctly implementing manual operation of thevehicle 10 under which the current straight ahead driving to approachthe intersection 1104 is being performed. Instead, depending on thescenario, the alert notifies the user that, under the manual operationof the vehicle 10, it incorrectly has a speed of two miles per houralong the roadway 1102, or the alert notifies the user that, under themanual operation of the vehicle 10, it incorrectly has a speed of sevenmiles per hour along the roadway 1102. The remedial instructions furthernotify the user that, under the manual operation of the vehicle 10, itshould have a speed of ten miles per hour along the roadway 1102.

In operations 914-920, as compliment to prompting the user to implementcorrective manual operation of the vehicle 10, the vehicle 10 promptsits own corrective autonomous operation under which its driving behaviormatches the predominating driving behavior of the like population ofreference vehicles.

In operation 914, while the vehicle 10 is still in the midst of manualoperation, the vehicle 10 makes an offer of autonomous operation underwhich the current actual driving maneuver being performed by the vehicle10, as well as impending driving maneuvers, will be performed. Uponposing the offer, the vehicle 10 waits for a user response to the offer.If the user wishes to implement corrective manual operation of thevehicle 10, the user response could be that the user does not accept theoffer. If this is the case, the process 900 returns to operation 902.However, if the user wishes for the vehicle 10 to implement correctiveautonomous operation of the vehicle 10, the user response will be thatthe user accepts the offer. If this is the case, the vehicle 10 alertsthe user of its impending corrective autonomous operation, in operation918, and initiates corrective autonomous operation of the vehicle 10,under which the current and impending driving maneuvers are performedaccording to the process 600, in operation 920. Rather than making theinitiation of corrective autonomous operation of the vehicle 10conditional on the user accepting an offer of autonomous operationaccording to operations 914 and 916, the process 900 could automaticallyproceed to operations 918 and 920 if, for instance, the driving behaviorof the vehicle 10 is atypical of the predominating driving behavior.

As shown in FIG. 11, the offer and the alert are issued to the user asoutputs 1130 at the surface of the windshield 58. Accordingly, theplanning/decision making module 94 may generate signals representingthese things as media transformable into visual outputs that may beprojected onto the surface of the windshield 58 by the projector 56 ofthe audio/video system 46. Although these things are described withreference to the outputs 1130 at the surface of the windshield 58,additionally, or alternatively, they could similarly be issued to theuser as an output 1130 at the interfaces implemented by the othercomponents of the audio/video system 46, such as its displays 54 and itsspeakers 52. The user response to the offer of autonomous operation isidentified from inputs received from the user at the various interfacesimplemented by the components of the audio/video system 46. Theplanning/decision making module 94 may, for instance, identify the userresponse to the offer of autonomous operation from input signalstransformed from corresponding verbal inputs detected by the microphones50. Similarly, the planning/decision making module 94 may, for instance,identify the user response to the offer of autonomous operation frominput signals transformed from corresponding mechanical inputs detectedby touch screens in the displays 54.

As shown with reference to outputs 1130 c and 1130 d, both the offer andthe alert include a notification of one or more impending drivingmaneuvers that will be performed under the corrective autonomousoperation of the vehicle 10. These could also include an analogousnotification for the current driving maneuver being performed by thevehicle 10. With the vehicle 10 maneuvering along the roadway 1102 asshown in FIG. 11, an impending driving maneuver may, for instance, bethe impending left-hand turn through the intersection 1104. As shownwith reference to the output 1130 c, the offer may pose the questionwhether the user wishes for the vehicle 10 to implement correctiveautonomous operation of the vehicle 10, while, as shown with referenceto the output 1130 d, the alert may include a notification thatcorrective autonomous operation of the vehicle 10 is impending.

Prompting Defensive Manual or Autonomous Operation.

According to a process 1000 shown in FIG. 10, the vehicle 10 and itsautonomous operation system 20 provide user assistance by promptingdefensive manual or autonomous operation of the vehicle 10 when thetraffic behaviors of objects in the environment surrounding the vehicle10 do not match the predominating traffic behavior of like populationsof reference objects, as described in traffic behavior models.

The process 1000 is, like the process 900, described with reference toFIG. 11, which shows an example perspective view of the user of thevehicle 10 out of its windshield 58, as well as conceptual renderings ofoutputs to the user at the various interfaces implemented by thecomponents of the audio/video system 46.

In operation 1002, information about the vehicle 10 is detected by thesensor system 60 and its sensors, or is otherwise received, for examplefrom the V2V communication system 76 and digital maps, for gathering andevaluation by the perception module 92, as described with reference tooperation 902 of the process 900.

In operation 1004, while the vehicle 10 is in the midst of manualoperation, the information about the vehicle 10 is further evaluated bythe perception module 92 to identify the traffic behavior of one or moreobjects in the environment surrounding the vehicle 10. With the vehicle10 maneuvering along the roadway 1102 as shown in FIG. 11, these objectsin the environment surrounding the vehicle 10 could include the oncomingvehicle 1112, the bicycle 1114 and the pedestrian 1116.

With the oncoming vehicle 1112, the bicycle 1114 and the pedestrian 1116being objects in the environment surrounding the vehicle 10, as part ofoperation 1004, the perception module 92 identifies one or more trafficmaneuvers being performed by these objects. Generally speaking, amongother information about the environment surrounding the vehicle 10, thetraffic maneuvers being performed by the objects are identified fromtheir location and motion. The identification of the traffic maneuversbeing performed by the objects is informed by information sourced fromdigital maps. This information includes the left-hand turn lane position1120 in which the vehicle 10 is located, the oncoming lane position 1124in which the oncoming vehicle 1112 is located, and the crossing laneposition 1126, among other lane positions, as well as traffic rulesincluding, among others, those dictated by the traffic light 1106.Additionally, or alternatively, this information could be sourced fromthe vehicle 10. The identification of the traffic maneuvers beingperformed by the objects is further informed by the location and motionof other objects in the environment surrounding the vehicle 10,including other obstacles to them, such as the vehicle 10 itself. Withthe oncoming vehicle 1112 maneuvering along the roadway 1102 as shown inFIG. 11, a driving maneuver may, for instance, be its current straightahead driving to traverse the intersection 1104. For the bicycle 1114off the roadway 1102, a biking maneuver may, for instance, be itscurrent stationary yielding to traffic in the intersection 1104.Similarly, for the pedestrian 1116 off the roadway 1102, a walkingmaneuver may, for instance, be its current stationary yielding totraffic in the intersection 1104.

Also as part of operation 1004, the perception module 92 quantifiesattributes of how the objects in the environment surrounding the vehicle10 perform the traffic maneuvers. These correspond to statisticallymedian or otherwise predominating attributes of how like populations ofreference objects performs the traffic maneuvers, as well as thestatistically outlying or otherwise atypical attributes of how the likepopulations of reference objects perform the traffic maneuvers, asdescribed in respective traffic behavior models for the objectsgenerated according to the process 300. For the oncoming vehicle 1112,for instance, the traffic behavior model describes the predominatingdriving behavior and the atypical driving behavior of a like populationof reference vehicles.

The process 1000 is applicable in principle to any objects in theenvironment surrounding the vehicle 10 for which traffic behavior modelshave been generated according to the process 300. In addition to theoncoming vehicle 1112, these objects include the bicycle 1114 and thepedestrian 1116. However, due to their current stationary yielding totraffic in the intersection 1104, the bicycle 1114 and the pedestrian1116 are not of interest to the vehicle 10 for purposes of prompting itsdefensive manual or autonomous operation. Accordingly, remainder of theprocess 1000 is described with reference to the oncoming vehicle 1112and the traffic behavior model generated for it.

In cases where, in the process 300, the information about the referenceobjects is sourced from the vehicle 10, operation 302 of the process 300may be performed in whole or in part in combination with operation 1002,and operations 304 and 306 of the process 300 may be performed in wholeor in part in combination with operation 1004. In these cases, thereference objects may include the flanking vehicle 1110, the oncomingvehicle 1112, the bicycle 1114 and the pedestrian 1116. In onboardimplementations of the process 300, where the process 300 is performedonboard the vehicle 10, operations 302-312 of the process 300 may beperformed, in real-time, in combination with operations 1002 and 1004,with the reference objects, once again, including the flanking vehicle1110, the oncoming vehicle 1112, the bicycle 1114 and the pedestrian1116.

In operation 1006, the planning/decision making module 94 identifieswhether the driving behavior of the oncoming vehicle 1112 matches thepredominating driving behavior.

As part of operation 1006, the planning/decision making module 94identifies whether the driving maneuvers being performed by the oncomingvehicle 1112 are the same as the driving maneuvers performed by the likepopulation of reference vehicles, as described in the traffic behaviormodel. Also as part of operation 1006, the planning/decision makingmodule 94 identifies, on an attribute-by-attribute basis, whether theattributes of how the oncoming vehicle 1112 performs the drivingmaneuvers match corresponding predominating attributes of how the likepopulation of reference vehicles performs the driving maneuvers. For anynon-matching attributes of how the oncoming vehicle 1112 performs adriving maneuver, the planning/decision making module 94 may alsoidentify whether they match corresponding atypical attributes of how thelike population of reference vehicles performs the driving maneuver.

If the driving behavior of the oncoming vehicle 1112 matches thepredominating driving behavior in all respects, the process 1000 returnsto operation 1002. On the other hand, if the driving behavior of theoncoming vehicle 1112 is atypical of the predominating driving behavior,or otherwise does not match the predominating driving behavior, in anyrespect, the vehicle 10, in operations 1008 and 1010, prompts the userto implement defensive manual operation of the vehicle 10 under whichthe driving behavior of the oncoming vehicle 1112 is addressed.

To prompt the user to implement defensive manual operation of thevehicle 10 if the driving behavior of the oncoming vehicle 1112 isatypical of the predominating driving behavior, the vehicle 10 warns orotherwise alerts the user of this in operation 1008. Similarly, inoperation 1010, if the driving behavior of the oncoming vehicle 1112 isnot atypical of the predominating driving behavior, but otherwise doesnot match the predominating driving behavior in any respect, the vehicle10 alerts the user of this to prompt the user to implement defensivemanual operation of the vehicle 10.

As shown in FIG. 11, the alerts prompting the user to implementdefensive manual operation of the vehicle 10 are issued to the user asoutputs 1130 at the surface of the windshield 58. Accordingly, theplanning/decision making module 94 may generate signals representingthese things as media transformable into visual outputs that may beprojected onto the surface of the windshield 58 by the projector 56 ofthe audio/video system 46. Although these things are described withreference to the outputs 1130 at the surface of the windshield 58,additionally, or alternatively, they could similarly be issued to theuser as outputs 1130 at the interfaces implemented by the othercomponents of the audio/video system 46, such as its displays 54 and itsspeakers 52.

Optionally, the alerts prompting the user to implement defensive manualoperation of the vehicle 10 could include a notification of whether thedriving maneuvers performed by the oncoming vehicle 1112 are the same asthe driving maneuvers performed by the like population of referencevehicles. Assuming this is the case, as shown with reference to outputs1130 e and 1130 f, the alerts prompting the user to implement defensivemanual operation of the vehicle 10 include notifications of one, some orall of the attributes of how the oncoming vehicle 1112 performs thedriving maneuvers. As additionally shown with reference to outputs 1130e and 1130 f, these things also include concurrent notifications, on anattribute-by-attribute basis, that the attributes of how the oncomingvehicle 1112 performs the driving maneuvers match corresponding atypicalattributes of how the like population of reference vehicles performs thedriving maneuvers, or otherwise do not match corresponding predominatingattributes of how the like population of reference vehicles performs thedriving maneuvers.

With the current straight ahead driving to traverse the intersection1104 being a driving maneuver, in an atypical scenario, as shown withreference to the output 1130 e, the alert may include a notificationthat the speed of the oncoming vehicle 1112 along the roadway 1102associated with its performance of the current straight ahead driving totraverse the intersection 1104 is fifty-five miles per hour and,accordingly, matches an atypical speed of the like population ofreference vehicles along roadways associated with its performance ofstraight ahead driving to traverse intersections. In a non-predominatingscenario, as shown with reference to the output 1130 f, the alert mayinclude a notification that the speed of the oncoming vehicle 1112 alongthe roadway 1102 associated with its performance of the current straightahead driving to traverse the intersection 1104 is forty miles per hourand, accordingly, does not match the predominating speed of the likepopulation of reference vehicles along roadways associated with itsperformance of straight ahead driving to traverse intersections.Regardless of the scenario, a concurrent notification may be included ofthe predicted future maneuvering of the oncoming vehicle 1112 along theroadway 1102, as predicted according to the process 400. Also, as shownwith reference to both the output 1130 e and the output 1130 f, toprovide context about the predominating driving behavior, a concurrentnotification may be included that the predominating speed of the likepopulation of reference vehicles along roadways associated with itsperformance of straight ahead driving to traverse intersections isthirty-five miles per hour. The alerts prompting the user to implementdefensive manual operation of the vehicle 10 could also include anycombination of analogous notifications of attributes of how the oncomingvehicle 1112 performs the driving maneuvers corresponding to otheratypical and predominating attributes of how the like population ofreference vehicles performs the driving maneuvers.

According to the alerts prompting the user to implement defensive manualoperation of the vehicle 10, the user is notified that the oncomingvehicle 1112 is not correctly implementing operation of the oncomingvehicle 1112 under which the current straight ahead driving to traversethe intersection 1104 is being performed. Instead, depending on thescenario, the alert notifies the user that, under the operation of theoncoming vehicle 1112, it incorrectly has a speed of fifty-five milesper hour along the roadway 1102, or the alert notifies the user that,under the operation of the oncoming vehicle 1112, it incorrectly has aspeed of forty miles per hour along the roadway 1102. The user is alsonotified of the predicted future maneuvering of the oncoming vehicle1112 along the roadway 1102. For context, the user is further notifiedthe user that, under the operation of the oncoming vehicle 1112, itshould have a speed of thirty-five miles per hour along the roadway1102.

In operations 1012-1018, as compliment to prompting the user toimplement defensive manual operation of the vehicle 10, the vehicle 10prompts its own defensive autonomous operation under which the drivingbehavior of the oncoming vehicle 1112 is addressed.

In operation 1012, while the vehicle 10 is still in the midst of manualoperation, the vehicle 10 makes an offer of autonomous operation underwhich the current driving maneuver being performed by the vehicle 10, aswell as impending driving maneuvers, will be performed. Upon posing theoffer, the vehicle 10 waits for a user response to the offer. If theuser wishes to implement defensive manual operation of the vehicle 10,the user response could be that the user does not accept the offer. Ifthis is the case, the process 1000 returns to operation 1002. However,if the user wishes for the vehicle 10 to implement defensive autonomousoperation of the vehicle 10, the user response will be that the useraccepts the offer. If this is the case, the vehicle 10 alerts the userof its impending defensive autonomous operation, in operation 1016, andinitiates defensive autonomous operation of the vehicle 10 under whichthe current and impending driving maneuvers are performed, for instanceaccording to the process 600, or another process tailored to defensiveautonomous operation, in operation 1018. In operation 1018, the currentand impending driving maneuvers may be performed according to theprocess 600. Rather than making the initiation of defensive autonomousoperation of the vehicle 10 conditional on the user accepting an offerof autonomous operation according to operations 1012 and 1014, theprocess 1000 could directly proceed to operations 1016 and 1018 if, forinstance, the driving behavior of the oncoming vehicle 1112 is atypicalof the predominating driving behavior.

As shown in FIG. 11, the offer and the alert are issued to the user asoutputs 1130 at the surface of the windshield 58. Accordingly, theplanning/decision making module 94 may generate signals representingthese things as media transformable into visual outputs that may beprojected onto the surface of the windshield 58 by the projector 56 ofthe audio/video system 46. Although these things are described withreference to the outputs 1130 at the surface of the windshield 58,additionally, or alternatively, they could similarly be issued to theuser as an output 1130 at the interfaces implemented by the othercomponents of the audio/video system 46, such as its displays 54 and itsspeakers 52. The user response to the offer of autonomous operation isidentified from inputs received from the user at the various interfacesimplemented by the components of the audio/video system 46. Theplanning/decision making module 94 may, for instance, identify the userresponse to the offer of autonomous operation from input signalstransformed from corresponding verbal inputs detected by the microphones50. Similarly, the planning/decision making module 94 may, for instance,identify the user response to the offer of autonomous operation frominput signals transformed from corresponding mechanical inputs detectedby touch screens in the displays 54.

As shown with reference to outputs 1130 c and 1130 d, both the offer andthe alert include a notification of one or more impending drivingmaneuvers that will be performed under the defensive autonomousoperation of the vehicle 10. These could also include an analogousnotification for the current driving maneuver being performed by thevehicle 10. With the vehicle 10 maneuvering along the roadway 1102 asshown in FIG. 11, an impending driving maneuver may, for instance, bethe impending left-hand turn through the intersection 1104. As shownwith reference to the output 1130 c, the offer may pose the questionwhether the user wishes for the vehicle 10 to implement defensiveautonomous operation of the vehicle 10, while, as shown with referenceto the output 1130 d, the alert may include a notification thatdefensive autonomous operation of the vehicle 10 is impending.

While recited characteristics and conditions of the invention have beendescribed in connection with certain embodiments, it is to be understoodthat the invention is not to be limited to the disclosed embodimentsbut, on the contrary, is intended to cover various modifications andequivalent arrangements included within the spirit and scope of theappended claims, which scope is to be accorded the broadestinterpretation so as to encompass all such modifications and equivalentstructures as is permitted under the law.

What is claimed is:
 1. A vehicle, comprising: at least one processor;and a memory communicably coupled to the at least one processor andstoring: a planning/decision making module including instructions thatwhen executed by the at least one processor cause the at least oneprocessor to: receive a traffic behavior model that describes apredominating driving behavior of a like population of referencevehicles, and while the vehicle is in the midst of manual operation,issue, at at least one interface, prospective instructions to a user onhow to make a driving behavior of the vehicle match the predominatingdriving behavior of the like population of reference vehicles; and aperception module including instructions that when executed by the atleast one processor cause the at least one processor to: evaluateinformation about the manual operation of the vehicle and informationabout an environment surrounding the vehicle, and based on theevaluation of the information about the manual operation of the vehicleand the information about the environment surrounding the vehicle,identify the driving behavior of the vehicle; wherein theplanning/decision making module further includes instructions that whenexecuted by the at least one processor cause the at least one processorto, in response to identifying that the driving behavior of the vehicledoes not match the predominating driving behavior of the like populationof reference vehicles: issue, at the least one interface, remedialinstructions to the user on how to make the driving behavior of thevehicle match the predominating driving behavior of the like populationof reference vehicles.
 2. The vehicle of claim 1, wherein evaluatinginformation about the environment surrounding the vehicle includes:identifying driving behaviors of reference vehicles in the environmentsurrounding the vehicle; identifying the like population of referencevehicles as those of the reference vehicles situated similarly to thevehicle for purposes of performing driving maneuvers; identifying thepredominating driving behavior of the like population of referencevehicles; and generating the traffic behavior model that describes thepredominating driving behavior of the like population of referencevehicles.
 3. The vehicle of claim 1, wherein: the traffic behavior modeldescribes driving maneuvers performed by the like population ofreference vehicles, and predominating attributes of how the likepopulation of reference vehicles performs the driving maneuvers; and theprospective instructions to a user on how to make the driving behaviorof the vehicle match the predominating driving behavior of the likepopulation of reference vehicles include a notification of an impendingtraining driving maneuver identified from among the driving maneuversperformed by the like population of reference vehicles, and anotification of at least one predominating attribute of how the likepopulation of reference vehicles performs the training driving maneuver.4. The vehicle of claim 3, wherein identifying the driving behavior ofthe vehicle includes: identifying an actual driving maneuver performedby the vehicle; and quantifying at least one attribute of how thevehicle performed the actual driving maneuver corresponding to thenotified at least one predominating attribute of how the like populationof reference vehicles performs the training driving maneuver.
 5. Thevehicle of claim 4, wherein the planning/decision making module includesinstructions that when executed by the at least one processor cause theat least one processor to identify that the driving behavior of thevehicle does not match the predominating driving behavior of the likepopulation of reference vehicles when the actual driving maneuverperformed by the vehicle is the same as the training driving maneuver,but the least one attribute of how the vehicle performed the actualdriving maneuver does not match the corresponding notified at least onepredominating attribute of how the like population of reference vehiclesperforms the training driving maneuver.
 6. The vehicle of claim 5,wherein the remedial instructions on how to make the driving behavior ofthe vehicle match the predominating driving behavior of the likepopulation of reference vehicles include a notification that the leastone attribute of how the vehicle performed the actual driving maneuverdoes not match the corresponding notified at least one predominatingattribute of how the like population of reference vehicles performs thetraining driving maneuver.
 7. The vehicle of claim 3, wherein thenotified at least one predominating attribute of how the like populationof reference vehicles performs the training driving maneuver includes atleast one of a predominating driving path, speed, acceleration andorientation of the like population of reference vehicles along roadwaysassociated with its performance of the training driving maneuver.
 8. Thevehicle of claim 3, wherein the notified at least one predominatingattribute of how the like population of reference vehicles performs thetraining driving maneuver includes at least one of a predominating laneoffset, proximity to obstacles on roadways and approach to obstacles onroadways associated with the performance of the training drivingmaneuver by the like population of reference vehicles.
 9. The vehicle ofclaim 3, wherein the predominating attributes of how the like populationof reference vehicles performs the driving maneuvers are thestatistically median attributes of how the like population of referencevehicles performs the driving maneuvers.
 10. A vehicle, comprising: atleast one processor; and a memory communicably coupled to the at leastone processor and storing: a perception module including instructionsthat when executed by the at least one processor cause the at least oneprocessor to: evaluate information about manual operation of the vehicleand information about an environment surrounding the vehicle, andidentify, based on the evaluation of the information about the manualoperation of the vehicle and the information about the environmentsurrounding the vehicle, a driving behavior of the vehicle; and aplanning/decision making module including instructions that whenexecuted by the at least one processor cause the at least one processorto: receive a traffic behavior model that describes a predominatingdriving behavior of a like population of reference vehicles, and inresponse to identifying that the driving behavior of the vehicle doesnot match the predominating driving behavior of the like population ofreference vehicles: issue, at at least one interface, an alert to a userprompting the user to implement corrective manual operation under whichthe driving behavior of the vehicle matches the predominating drivingbehavior of the like population of reference vehicles.
 11. The vehicleof claim 10, wherein evaluating information about the environmentsurrounding the vehicle includes: identifying driving behaviors ofreference vehicles in the environment surrounding the vehicle;identifying the like population of reference vehicles as those of thereference vehicles situated similarly to the vehicle for purposes ofperforming driving maneuvers; identifying the predominating drivingbehavior of the like population of reference vehicles; and generatingthe traffic behavior model that describes the predominating drivingbehavior of the like population of reference vehicles.
 12. The vehicleof claim 10, wherein the planning/decision making module includesinstructions that when executed by the at least one processor cause theat least one processor to, in response to identifying that the drivingbehavior of the vehicle does not match the predominating drivingbehavior of the like population of reference vehicles: issue, at theleast one interface, remedial instructions to the user on how to makethe driving behavior of the vehicle match the predominating drivingbehavior of the like population of reference vehicles.
 13. The vehicleof claim 10, wherein: the traffic behavior model describes drivingmaneuvers performed by the like population of reference vehicles, andpredominating attributes of how the like population of referencevehicles performs the driving maneuvers; and the planning/decisionmaking module includes instructions that when executed by the at leastone processor cause the at least one processor to identify that thedriving behavior of the vehicle does not match the predominating drivingbehavior of the like population of reference vehicles when an actualdriving maneuver performed by the vehicle is the same as a drivingmaneuver identified from among the driving maneuvers performed by thelike population of reference vehicles, but at least one attribute of howthe vehicle performed the actual driving maneuver does not match acorresponding at least one predominating attribute of how the likepopulation of reference vehicles performs the driving maneuver.
 14. Thevehicle of claim 13, wherein the alert to a user prompting the user toimplement corrective manual operation under which the driving behaviorof the vehicle matches the predominating driving behavior of the likepopulation of reference vehicles includes a notification that the leastone attribute of how the vehicle performed the actual driving maneuverdoes not match the corresponding at least one predominating attribute ofhow the like population of reference vehicles performs the drivingmaneuver.
 15. The vehicle of claim 13, wherein the predominatingattributes of how the like population of reference vehicles performs thedriving maneuvers are the statistically median attributes of how thelike population of reference vehicles performs the driving maneuvers.16. A vehicle, comprising: at least one processor; and a memorycommunicably coupled to the at least one processor and storing: aperception module executable by at least one processor includinginstructions that when executed by the at least one processor cause theat least one processor to: while the vehicle is in the midst of manualoperation, evaluate information about an environment surrounding thevehicle, and identify, based on the evaluation of the information aboutthe environment surrounding the vehicle, a traffic behavior of an objectin the environment surrounding the vehicle; and a planning/decisionmaking module including instructions that when executed by the at leastone processor cause the at least one processor to: receive a trafficbehavior model that describes a predominating traffic behavior of a likepopulation of reference objects, and in response to identifying that thetraffic behavior of the object does not match the predominating trafficbehavior of the like population of reference objects: issue, at at leastone interface, an alert to a user prompting the user to implementdefensive manual operation under which the traffic behavior of theobject is addressed.
 17. The vehicle of claim 16, wherein evaluatinginformation about the environment surrounding the vehicle includes:identifying traffic behaviors of reference objects in the environmentsurrounding the vehicle; identifying the like population of referenceobjects as those of the reference objects situated similarly to theobject for purposes of performing traffic maneuvers; identifying thepredominating traffic behavior of the like population of referenceobjects; and generating the traffic behavior model that describes thepredominating traffic behavior of the like population of referenceobjects.
 18. The vehicle of claim 16, wherein: the traffic behaviormodel describes traffic maneuvers performed by the like population ofreference objects, and predominating attributes of how the likepopulation of reference objects performs the traffic maneuvers; and theplanning/decision making module includes instructions that when executedby the at least one processor cause the at least one processor toidentify that the traffic behavior of the object does not match thepredominating traffic behavior of the like population of referenceobjects when a traffic maneuver performed by the object is the same as atraffic maneuver identified from among the traffic maneuvers performedby the like population of reference objects, but at least one attributeof how the object performed the traffic maneuver does not match acorresponding at least one predominating attribute of how the likepopulation of reference objects performs the traffic maneuver.
 19. Thevehicle of claim 18, wherein the alert to a user prompting the user toimplement defensive manual operation under which the traffic behavior ofthe object is addressed includes a notification that the least oneattribute of how the object performed the traffic maneuver does notmatch the corresponding at least one predominating attribute of how thelike population of reference objects performs the traffic maneuver. 20.The vehicle of claim 18, wherein the predominating attributes of how thelike population of reference objects performs the traffic maneuvers arethe statistically median attributes of how the like population ofreference objects performs the traffic maneuvers.
 21. A method ofproviding user assistance in a vehicle, comprising: receiving a trafficbehavior model that describes a predominating driving behavior of a likepopulation of reference vehicles; while the vehicle is in the midst ofmanual operation, issuing, at at least one interface, prospectiveinstructions to a user on how to make a driving behavior of the vehiclematch the predominating driving behavior of the like population ofreference vehicles; evaluating information about the manual operation ofthe vehicle and information about an environment surrounding thevehicle; identifying, based on the evaluation of the information aboutthe manual operation of the vehicle and the information about theenvironment surrounding the vehicle, the driving behavior of thevehicle; and in response to identifying that the driving behavior of thevehicle does not match the predominating driving behavior of the likepopulation of reference vehicles: issuing, at the least one interface,remedial instructions to the user on how to make the driving behavior ofthe vehicle match the predominating driving behavior of the likepopulation of reference vehicles.
 22. The method of claim 21, whereinevaluating information about the environment surrounding the vehicleincludes: identifying driving behaviors of reference vehicles in theenvironment surrounding the vehicle; identifying the like population ofreference vehicles as those of the reference vehicles situated similarlyto the vehicle for purposes of performing driving maneuvers; identifyingthe predominating driving behavior of the like population of referencevehicles; and generating the traffic behavior model that describes thepredominating driving behavior of the like population of referencevehicles.
 23. The method of claim 21, wherein: the traffic behaviormodel describes driving maneuvers performed by the like population ofreference vehicles, and predominating attributes of how the likepopulation of reference vehicles performs the driving maneuvers; and theprospective instructions to a user on how to make the driving behaviorof the vehicle match the predominating driving behavior of the likepopulation of reference vehicles include a notification of an impendingtraining driving maneuver identified from among the driving maneuversperformed by the like population of reference vehicles, and anotification of at least one predominating attribute of how the likepopulation of reference vehicles performs the training driving maneuver.24. A method of providing user assistance in a vehicle, comprising:evaluating information about manual operation of the vehicle andinformation about an environment surrounding the vehicle; identifying,based on the evaluation of the information about the manual operation ofthe vehicle and the information about the environment surrounding thevehicle, a driving behavior of the vehicle; receiving a traffic behaviormodel that describes a predominating driving behavior of a likepopulation of reference vehicles; and in response to identifying thatthe driving behavior of the vehicle does not match the predominatingdriving behavior of the like population of reference vehicles: issuing,at at least one interface, an alert to a user prompting the user toimplement corrective manual operation under which the driving behaviorof the vehicle matches the predominating driving behavior of the likepopulation of reference vehicles.
 25. The method of claim 24, whereinevaluating information about the environment surrounding the vehicleincludes: identifying driving behaviors of reference vehicles in theenvironment surrounding the vehicle; identifying the like population ofreference vehicles as those of the reference vehicles situated similarlyto the vehicle for purposes of performing driving maneuvers; identifyingthe predominating driving behavior of the like population of referencevehicles; and generating the traffic behavior model that describes thepredominating driving behavior of the like population of referencevehicles.
 26. The method of claim 24, further comprising: in response toidentifying that the driving behavior of the vehicle does not match thepredominating driving behavior of the like population of referencevehicles: issuing, at the least one interface, remedial instructions tothe user on how to make the driving behavior of the vehicle match thepredominating driving behavior of the like population of referencevehicles.
 27. The method of claim 24, wherein the traffic behavior modeldescribes driving maneuvers performed by the like population ofreference vehicles, and predominating attributes of how the likepopulation of reference vehicles performs the driving maneuvers, furthercomprising: identifying that the driving behavior of the vehicle doesnot match the predominating driving behavior of the like population ofreference vehicles when an actual driving maneuver performed by thevehicle is the same as a driving maneuver identified from among thedriving maneuvers performed by the like population of referencevehicles, but at least one attribute of how the vehicle performed theactual driving maneuver does not match a corresponding at least onepredominating attribute of how the like population of reference vehiclesperforms the driving maneuver.
 28. The method of claim 27, wherein thealert to a user prompting the user to implement corrective manualoperation under which the driving behavior of the vehicle matches thepredominating driving behavior of the like population of referencevehicles includes a notification that the least one attribute of how thevehicle performed the actual driving maneuver does not match thecorresponding at least one predominating attribute of how the likepopulation of reference vehicles performs the driving maneuver.
 29. Amethod of providing user assistance in a vehicle, comprising: while thevehicle is in the midst of manual operation, evaluating informationabout an environment surrounding the vehicle; identifying, based on theevaluation of the information about the environment surrounding thevehicle, a traffic behavior of an object in the environment surroundingthe vehicle; receiving a traffic behavior model that describes apredominating traffic behavior of a like population of referenceobjects; and in response to identifying that the traffic behavior of theobject does not match the predominating traffic behavior of the likepopulation of reference objects: issuing, at at least one interface, analert to a user prompting the user to implement defensive manualoperation under which the traffic behavior of the object is addressed.30. The method of claim 29, wherein evaluating information about theenvironment surrounding the vehicle includes: identifying trafficbehaviors of reference objects in the environment surrounding thevehicle; identifying the like population of reference objects as thoseof the reference objects situated similarly to the object for purposesof performing traffic maneuvers; identifying the predominating trafficbehavior of the like population of reference objects; and generating thetraffic behavior model that describes the predominating traffic behaviorof the like population of reference objects.
 31. The method of claim 29,wherein the traffic behavior model describes traffic maneuvers performedby the like population of reference objects, and predominatingattributes of how the like population of reference objects performs thetraffic maneuvers, further comprising: identifying that the trafficbehavior of the object does not match the predominating traffic behaviorof the like population of reference objects when a traffic maneuverperformed by the object is the same as a traffic maneuver identifiedfrom among the traffic maneuvers performed by the like population ofreference objects, but at least one attribute of how the objectperformed the traffic maneuver does not match a corresponding at leastone predominating attribute of how the like population of referenceobjects performs the traffic maneuver.
 32. The method of claim 31,wherein the alert to a user prompting the user to implement defensivemanual operation under which the traffic behavior of the vehicle isaddressed includes a notification that the least one attribute of howthe object performed the traffic maneuver does not match thecorresponding at least one predominating attribute of how the likepopulation of reference objects performs the traffic maneuver.